COMPUTER VISION: A REVIEW OF TECHNIQUES AND ADVANCEMENTS

International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 32 COMPUTER VISION: A REVIEW OF TECHNIQUES AND ADVANCEMENTS Dr. Jaya Sharma1 1HOD Dept. of Computer Science and IT College of Professional Studies Ambikapur, Surguja, Chhattisgarh, India However, today, with much research and technological advancements in computer science, computer vision has grown to be a cornerstone of modern artificial intelligence since it empowers machines to perceive, interpret, and understand visual information from the world around them. By doing so, it enables various systems to process images and videos much like human sight, further helping in advancing many domains. The development of computer vision has had a gradual pace starting with basic techniques such as edge detection, filtering, and segmentation, all with the intent of accomplishing image processing and feature extraction. Over time, these methodologies evolved with the rise of machine learning to more robust systems that could recognize objects, faces, and scenes. The real breakthrough came with deep learning; that is, convolutional neural networks have revolutionized tasks such as image classification and object detection. This is not to say there are no other innovations, with the advent of recurrent neural networks for sequential vision tasks, Generative Adversarial Networks for synthesizing images, and recently Vision Transformers for large-scale visual understanding. Together with these models, large datasets, such as ImageNet, COCO, and Open Images, and benchmarks like mAP and F1-scores, were crucial in measuring progress, fostering competition. Despite these impressive successes, challenges persist within this area. Such challenges will include those to do with high computational cost, model interpretability, ethical issues on bias, privacy, and surveillance. Yet, emerging directions such as multimodal learning, edge AI, and self-supervised learning present promise for the road ahead. Overall, computer vision remains one of the fastest-evolving areas, offering an exciting opportunity for people, researchers, engineers, and students to make an impact on how machines will perceive and interact with the visual world. Keywords: Computer Vision, Deep Learning, Image Processing, Object Detection, Convolutional Neural Networks (CNNs), Vision Transformers, Datasets, Evaluation Metrics, Challenges, Future Directions I. INTRODUCTION Computer Vision has emerged as a critical area of AI, enabling machines to interpret, analyze, and learn from visual data in the form of images and videos. By granting computers the ability to "see," it has completely transformed agriculture, healthcare, security, manufacturing, and autonomous transport. In farming, CV-based tools play an important role in the monitoring of crops, the detection of crop diseases, and seed quality analysis as it helps farmers make informed decisions and adopt more sustainable and efficient practices compared to traditional methods [1]. Likewise, in healthcare, CV-driven medical imaging systems help doctors in the identification, classification, and segmentation of diseases with high accuracy and speed. However, in spite of these technological advantages, realworld implementation in many hospitals is still limited due to great computational requirements, data privacy, and poor digital infrastructure [2]. The fast evolution of deep learning has been the key driver behind modern computer vision capabilities. Among all the others, a wide range of Convolutional Neural Networks have revolutionized how visual information is processed, enabling state-of-the-art models for object detection, image recognition, and semantic segmentation. These approaches now enable complex real-world applications in real-time, like surgical guidance, where CV systems will support surgeons by enhancing precision, reducing human error, and improving decision-making intra-operatively [3]. This review summarizes the advances along the development path of computer vision—from old rule-based and feature extraction-based methods to contemporary deep learning and transformer-based approaches. Then, it summarizes most of the widely used datasets, such as ImageNet and COCO, together with the related evaluation metrics that benchmark the performance of the models. It further discusses International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 33 persistent challenges including computational costs, model interpretability, and ethical issues like data bias and surveillance. Finally, new directions such as Edge AI, multimodal learning, and self-supervised training are explored as major keys to the future direction that computer vision research, along with its practical applications, will go towards. II. BACKGROUND AND MOTIVATION Computer vision has changed a lot over the years. Most of it was about simple image processing. Researchers had to design features by hand and then plug them into statistical models. These methods worked fine on small cases but failed when the data became big or too complex. Deep learning flipped this game. Instead of relying on handcrafted features, models started learning directly from raw images: it is able to create new data form existing one and change the game of computer vision. This helped them beat older techniques in almost every area [4]. Major contributions were made by CNNs, Deep Belief Networks, and Autoencoders. They proved quite useful for face recognition, object detection, pose estimation, and activity tracking. An important advantage was that they learned patterns themselves, and people no longer need to design every feature manually. Then came specialized models. YOLO, short for You Only Look Once, became famous for object detection in real time. In his last version, YOLOv8, is faster and more accurate; hence, it becomes indispensable in robotics, medical scans, and auto-drive cars [5]. As technology advanced, the new challenges showed up more. The most strong was the problem of handling 3D data. Of course, two-dimensional vision is still quite common, but robotics, surgery, and augmented reality often require three-dimensional information. Some 3D models based on voxels face limits, while 2D projections still work quite well. However, better datasets and higher-resolution inputs are required to go up to the next level [6]. The jump from hand-designed features to deep learning models transformed computer vision. Today, faster models, real-time systems, and smarter ways to handle complex types of data mark today's headline. III. EVOLUTION OF COMPUTER VISION Computer vision has undergone steady advancement characterized by new methodologies and broader applications. At its inception computer vision work was focused on manageable image processing tasks such as edge detection, segmentation, and pattern matching. Often, these early technologies used mostly hand-crafted features and statistical based tools; While these early technologies were useful for a starting point, they were still not strong enough to accommodate disorganized measurements in real-world data. Eventually, the field shifted from manual rules to deep learning models that could learn features at several levels directly on the raw images. In practice, computer vision is now being utilized to enhance worker safety. One common application is monitoring the appropriate use of Personal Protective Equipment (PPE) by workers. The systems can monitor compliance and track behaviors in real-time. However, challenges still arise when delving into this new practice. The costs of computing, changing environments, and identity management barriers to large-scale use exists. These barriers have spurred a push to find cheaper, and more scalable solutions that can operate under certain conditions [7]. Another relatively new field that computer vision has been adapted for is network security. At first, it may not seem plausible to add computer vision here but vision-based methods have been molded to detect phishing attempts, identify malware, and detect anomalous traffic as well. In particular, convolutional neural networks are consistently used in cybersecurity often replacing traditional intrusion-detection systems. IV. CORE TECHNIQUES IN COMPUTER VISION 1. Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNNs) lend themselves to contemporary computer vision because they have the ability to acquire layered visual features automatically through supervised training. Their influence over the past decade has been significant in image classification, recognition, and segmentation, and the applications of CNNs are now present in diverse settings from sport analytics, such as player detection, trajectory tracking, and event recognition, to agriculture, where they assist with crop monitoring, disease diagnosis, and yield predictions. 2. Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks in particular, are beneficial whenever visual data develops over time. While CNNs are highly effective in dealing with still images, RNNs are designed for modelling sequential data, making them well suited for analyzing temporal outcomes. Practically, International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 34 which a sport context, RNNs can learn patterns of ball movement and the strategies of players as a function of gametime. In the agricultural literature, for example, a combination of RNNs and CNNs have been useful to model the temporal progression of crop growth using images, and a likewise outcome in crossing domains can offer valuable forecasting utility. 3. Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) have provided new avenues by creating synthetic data. This is especially beneficial in domains with small or unbalanced datasets. In sports, GANs have been used for enhancing lowresolution video and improving tracking, and in agriculture, GANs have generated plant images using synthetic data, improving the strength of classifiers used for disease or pests. These applications illustrate how data generation can be just as important as data analysis, with the data being generated from a different source [10][11]. 4. Vision Transformers (ViTs) Vision Transformers (ViTs) have aimed to be an alternative to CNNs. ViTs treat an image as a succession of patches, which enables them to find long-range relationships that CNNs generally miss. They are now showing strong performance on tasks such as event analysis in sports and fine-grained plant disease detection in controlled environments, such as greenhouses. 5. Object Detection, Recognition, and Segmentation Object detection and segmentation systems are still prevalent, including YOLO, Faster R-CNN and Mask R-CNN. In sport, they can identify players and equipment in real time, assist with trajectory predictions, and even assist constructing automated sports highlights. Similarly, in agriculture, these methods assist in fruit detection, segmenting diseased leaves, and tracking plant growth. Table 1 Comparison of Core Computer Vision Techniques Technique Key Features Strengths Weaknesses Applications CNNs Convolutional layers, hierarchical feature extraction High accuracy, automatic feature learning Requires large datasets, high computation Image classification, object detection, medical imaging RNNs (LSTM/GRU) Sequential modelling, memory of past states Good for temporal data Vanishing gradient, slower training Video analysis, trajectory prediction, action recognition GANs Generatordiscriminator framework Data augmentation, realistic synthesis Training instability, mode collapse Data augmentation, superresolution, synthetic sports/medical images Vision Transformers Self-attention mechanism, patchbased processing Captures long-range dependencies, scalable Needs large datasets, high compute Image recognition, multimodal CV-NLP tasks, agricultural classification V. DATASETS AND BENCHMARKS IN COMPUTER VISION Datasets are foundational in computer vision. Datasets provide the examples required for the model to learn and serve as a means to provide a fair comparison of different ways of approaching a problem. Without good datasets, it becomes hard to show if a model is genuinely improving or not. This section will cover the significance of datasets in computer vision particularly in the fields of monitoring infrastructure and social media analysis currently, as well as highlight challenges and future directions of dataset development.  Significance of Datasets in Computer Vision Development): For models to learn, they must see examples. In computer vision, this means coverage of lots of images with labels. You may have a dataset in the context of the infrastructure, where the datasets serves to identify cracks, broken components, etc., in International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 35 bridges or tunnels. And you likely have datasets that can identify biased or misleading images in social media posts. In short, the more varied and well-labelled the dataset, the better the model works.  Benchmark Datasets - Infrastructure Applications : In the infrastructure context, bridges, pipelines, or tunnels, experts collect images and have highlighted defects for example, cracks, rust, or damaged material. This data can now be utilized to train models to automatically classify or identify issues which will save time and minimize human errors. Benchmarks allow researchers to evaluate systems, from simple CNN's to cutting-edge vision transformers, to determine the best approach to classification [12].  Benchmark Datasets - Social Media Applications: Social media is a little more complicated. Images are not complete since they are delivered with metadata, captions, and often edited, which can be misleading. Datasets in this context may be labelled for harmful content, misleading or manipulated images or biased images. This data may be used to develop models to help to explain model output, to detect fake content, to improve trust when interacting digitally, etc [13].  Issues Related to Dataset Collection and Benchmarking: Collecting datasets is rarely simple, and they can be difficult or even dangerous to collect in the case of infrastructure datasets. Social media datasets face issues of privacy and ethical collection. Models trained with a small or very specific dataset may fail to generalize to other contexts.  Future Trends and Directions: Prospects look bright for synthetic and multimodal datasets. Generative adversarial networks (GANs) or simulations can create data when real samples are limited. The use of images with either text or sensor data can lead to more robust models. This should be beneficial whether it is for a real-world monitoring effort or an ethical AI system operating on the web. VI. APPLICATIONS OF COMPUTER VISION Computer vision has transitioned from its initial testing stages and is now driving innovation in virtually all sectors. Its ability to process, analyse and interpret visual data at a massive scale has fostered innovation in healthcare, autonomous systems, manufacturing, agriculture and digital services. The application of computer vision exists in more than research facilities and is being incrementally rolled into applications, such as smart surveillance and selfdriving cars. This chapter highlights the leading application areas that have adopted computer vision technology and how they continue to rely on deep learning methods and techniques.  Healthcare and Medical Imaging: Computer vision has become a fundamental component of the healthcare industry for the processing of medical images, early disease detections and assisting surgical nations. Applications such as tumour detection, organ segmentation, and automatic silicon detection and report generation is an increasingly occurring using CNN-based architectures. These models could potentially exceed human capabilities in radiology by exploring the relationship between large image volumes more quickly without sacrificing accuracy. Beyond diagnostics, computer vision technologies are assisting in image-guided robotic-assistance surgery, while offering improved accuracy and patient safety over traditional surgical techniques.  Agriculture and Environmental Monitoring: Agriculture has greatly advanced from vision-based technologies. In particular, precision agriculture has benefitted more than any other application domain. Computer vision models are first being used for agricultural monitoring, weed detection, and yield estimation. CNN-based systems operated in greenhouses and controlled agricultural contexts autonomously recognize plant diseases and improve conditions for plant growth, increasing agricultural productivity [14]. Environmental monitoring applications rely on computer vision to monitor deforestation, understand the impacts of climate change, and assess biodiversity, through automated processing of satellite imagery.  Autonomous Vehicles and Robotics: Computer vision is foundational to autonomous vehicles and robots, where recognizing objects, planning paths, and navigating in the environment are fundamental capabilities. Vision-based algorithms allow vehicles to recognize pedestrians, traffic signs and obstacles in uncertain and dynamic natural environments. Visual SLAM (Simultaneous Localisation and Mapping) has become especially important in recent years because they enable robots or autonomous vehicles to localise themselves in the world while creating a map of their environment at the same time. Modern CNN-based International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 36 detectors and energy-efficient architectures are enabling these systems to be more and more reliable in realtime applications [15].  Industrial Automation and Manufacturing: The manufacturing industries have embraced computer vision technology, notably for fault detection, quality assurance, and predicative maintenance. Automated inspection systems can identify even tiny faults in products, ensuring higher quality assurance while reducing human involvement. In assembly lines, vision-based robots can confidently perform tasks such as sorting objects, placing parts, and welding with a reduced chance of making mistakes. The implementation of AI-based vision models enables the analysis of previous data for predictive analytics so companies can anticipate when equipment might fail and reduce downtime.  Security, Surveillance, and Smart Cities: Many smart cities are developing vision-based surveillance systems for traffic management, anomaly detection, and public safety. Computer vision image processing algorithms monitor the traffic flow to detect violations and identify undesirable incidents in real-time. Facial recognition technology is becoming increasingly popular for verification and accessing buildings or events. However, many of these systems offer ethical dilemmas around privacy, bias, and dependability, making transparency and explainability important for responsible uses of camera-enabled vision systems.  Entertainment and Sports Analytics: Computer vision provides a critical tool for enriching user experiences, from virtual reality gaming to live sports analytics. Vision systems are used in sports analytics for tracking players, performance evaluation, and automated highlight creation. In entertainment, techniques used for motion capture, augmented reality (AR), and virtual-reality (VR) depend on robust precision vision-based motion tracking systems. VII. RELATED WORK Computer vision research covers a broad range of fields, including general image analysis and applied areas in transportation, industry, and manufacturing. By investigating the relevant literature, it is clear that convolutional neural networks (CNNs), deep learning, and hybrid models have had a remarkable impact across a range of fields. Despite some barriers to entry, including issues of model interpretability, data privacy, and adaptation to domains, CNNs have shown us exceptional performance in automatic feature extraction and image classification, with applications in healthcare, agriculture, ecology, and industrial inspection [16]. Environmental computer vision systems have made visual data analysis more reliable and efficient, and we often use these systems' outputs for critical decision-making in our industries. In the context of intelligent transportation systems (ITS), there is increasing reliance on computer vision technologies for safer and more efficient traffic systems. Vision-based technologies are often used directly to provide the detection of vehicles and pedestrians, license plate recognition, traffic flow prediction, and traffic signal optimization. Vision-based systems also provide important components for autonomous driving and smart city infrastructure. However, further challenges remain, including constraints in delivery of real-time response, reliable processing capabilities associated with system scalability, and performance across a wide range of environments [17]. Research merging artificial intelligence-based vision and expert systems has furthered the field by implementing visual data analysis with computer graphics and vision technologies. This integration allows machines to see and analyze visual data more like a human level of understanding. Deep learning and CNN models are central to the realization of intelligent systems; however, the models often require a lot of data and constant training in order to have credible and reliable testing in the actual environments [18]. In textile manufacturing, computer vision has changed the traditional inspection process by automating defect detection, texture inspections, and pattern recognition. CNNs, decision-tree models, and ResNet-based methodologies have been used to improve accuracy and efficiency. However, the difficulty of implementing a cost-effective solution with real-time scalability in a largescale production environment creates a challenge [19]. All these studies encapsulate the breadth of research on computer vision and the multidisciplinary areas including medical diagnostics, industrial automation, and transportation. Despite tremendous advancements, challenges with model explainability, computational efficiency, or scalability persist. This review builds on prior surveys with recent advances in CNNs, transformers, and mixed methodology, and identifies gaps for future research. International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 37 VIII. EVALUATION METRICS Assessing and evaluating the performance of computer vision algorithms is critical to ensure their reliability, fairness, and flexibility in a wider range of applications. Measures provide quantitative significances for evaluating models, comparing alternate methods, and guiding future work. Without some type of rigorous evaluation, it is impossible to know whether an algorithm is genuinely performing better than simply fitting to its training data. There are many measures that have been established, often depending on the specific task: classification, detection, segmentation, or tracking – each with their own advantages, disadvantages and trade-offs.  Image Classification Measures: There are a number of evaluation measures that have been created that suit specific tasks such as classification, detection, segmentation, or tracking. For image classification, the common measures include: accuracy, precision, recall, F1-score, and top-k accuracy. First, accuracy refers to the fraction of predicted samples that were correctly categorized over all of the samples in the dataset. Precision generally refers to the correctness of positive predictions, whereas recall is more focused on the model's ability to find all of the correct positive instances. The F1 score is the harmonic mean of precision and recall, and is very useful when working with imbalanced datasets. Top-k accuracy is a useful measure for evaluating multi-class accuracy and indicates whether the right label is in the top-k number of predictions.  Object Detection Metrics: In object detection, there are several important metrics to measure performance, such as Intersection over Union (IoU), mean Average Precision (mAP), and Average Recall (AR). IoU is used to quantify the level of overlap between the predicted bounding boxes and ground truth bounding boxes as a basic measure of accuracy. The mAP metric combines precision and recall across multiple IoU thresholds into a single quality score of performance, while AR assesses how effectively a model detects objects of varying sizes, under different levels of overlap.  Segmentation Metrics: When evaluating image segmentation, commonly used metrics include pixel accuracy, mean Intersection over Union (mIoU), and the Dice coefficient. Pixel accuracy is a count of the proportion of pixels that have been accurately labeled. The mIoU takes the previous IoU definition, which is limited to detection and classification, and averages the IoU across all classes in semantic segmentation cases. Finally, the Dice coefficient is a commonly quantify prediction versus true region overlap to assess models, especially in the applications of medical imaging, where is known as a sensitive measure of overlap.  Tracking and Temporal Metrics: When assessing an object tracking task or a temporal vision task, MOTA and MOTP are the conventional metrics used to evaluate object tracking, that assess both the trajectory accuracy of objects, as well as the spatially accurate alignment precision. In addition to MOTA and MOTP, the number of identity switches (ID Switches) is noted, which is used mostly to note the number of times an object lost its identity across a series of frames, or changes the identity assigned to them.  Other Considerations: Alongside task-specific metrics, parameters such as inference duration, memory utilisation, and energy efficiency are gaining significance. Practical applications require models that are both precise and computationally viable. Evaluation metrics provide as a guiding framework for computer vision research, enabling the community to assess advancements, pinpoint deficiencies, and expand the capabilities of algorithms. The meticulous selection of measures guarantees that models are not only theoretically robust but also actually effective across diverse domains. IX. CHALLENGES AND LIMITATIONS Despite making amazing developments in computer vision, executing this technology in the real world remains very challenging. Research is still producing unprecedented advances, however, once implemented in the wild, the full picture negatives become apparent. These limitations occur in a technical, ethical, and operational context that exposes the disparity between laboratory validation and successful implementation. International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 38  Data-related Challenges: Data-related challenges should be considered some of the most basic problems. Most computer vision models require an enormous amount of high-quality, annotated data, which are not easy, cheap, or quick to obtain. Furthermore, it displays a bias or even an imbalance, as they tend to overrepresent certain populations, environments, or contexts, and results lead to unfair or incorrect predictions, which is especially pertinent in high-stakes instances including healthcare or law enforcement. Domain adaptation is another hurdle, as models trained on one particular data set, often can not generalize to another dataset due to changes in light, backgrounds, or other changes in the image quality clash with accuracy.  Computational Constraints: Computational constraints can also provide considerable challenges. Training a new deep learning architecture demands significant computation, which can encompass expensive GPUs and other high-stakes memory requirements — that can be limited for smaller institutions and developing areas. On the other hand, deploying computer vision models for real-time scenarios (e.g., autonomous driving, video surveillance, etc.) can be a matter of balancing speed and accuracy, which is no easy task with any degree of efficiency.  Interpretability and Transparency: Another major concern is the interpretability and transparency of any model. A majority of computer vision systems are "black boxes," in that they yield decisions that are difficult to explain or rationalize. Hypothetically, this lack of understanding can affect trust and adoption of models, particularly in a high-stakes field like medicine or the safety of a public, where the reasoning of each model is necessary for validation and utilization.  Generalization and Robustness: Aside from interpreting and transparency, issues of generalization and robustness can complicate deployment. Models that can accurately and reliably predict in controlled or benchmarked spaces, more often than not will not perform well in new or unknown spaces. Despite models working well in those settings (controlled or benchmarked), there are always extenuating outside variables that will limit the performance of the system, like image noise, occlusion, or deliberate adversarial attack.  Considerations regarding ethics and privacy: We need to consider ethical and privacy implications as well. The proliferation of cameras and vision-based monitoring systems raises valid concerns about surveillance, personal privacy, and data protection. Likewise, datasets containing personal images raise questions of consent, data ownership, and responsible use.  Deployment and maintenance: Finally, deployment and maintenance pose continual challenges. After development, models require continual adaptation to new contexts, integration with other technologies, and resilience to cyber threats. Long-term reliability, as well as compliance with ethical frameworks, necessitates continual monitoring and updates. Overall, even as computer vision continues to evolve rapidly, these challenges highlight the need for more diverse datasets that are representative and interpretable for end-users, energy-efficient algorithms, and ethical oversight. These issues must be resolved to develop computer vision systems that are powerful, and accurate, and socially responsible. Table 2 Challenges in Computer Vision and Potential Solutions Challenge Description Possible Solutions Data Quality & Imbalance Datasets often biased or limited Transfer learning, data augmentation, federated learning High Computational Cost Training deep models requires large resources Model compression, pruning, quantization, edge deployment Interpretability Models act as black boxes Explainable AI (XAI), attention visualization Robustness & Poor performance in unseen Domain adaptation, ensemble models, International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 39 Generalization conditions adversarial training Ethical & Privacy Concerns Misuse in surveillance, biased datasets Fairness-aware AI, privacy-preserving ML (e.g., FL, DP) Maintenance & Deployment Models degrade in dynamic environments Continuous learning, online updating X. RECENT TRENDS AND FUTURE DIRECTIONS The area of computer vision is progressing rapidly as researchers and practitioners explore new applications and solve old problems. Current trends suggest an increased reliance on deep learning frameworks, integration with other AI technologies, and applications in areas requiring real-time, efficient, and contextual systems. Future opportunities will be focused on technological advancements, scalability, interpretability, and ethical implications. One area of development includes sports video analysis, in which automated techniques provide insights for both amateurs and professionals. These systems can perform functions such as player detection, ball tracking, tactical analysis, event detection, and video summarization. About actual sport environments, this is a challenging area due to unpredictable activity, occlusion, camera instability, and image quality. However, the advancement of detection and tracking models is facilitating development in areas such as referee decision support, highlight generation, and virtual content [20]. A contemporary area of research addresses fundamental questions in machine learning methods applied to computer vision. Challenges remain with problems such as data asymmetry, low supervised data, blurriness of images, and high costs of training methods [21]. Several new trends are influencing the future of computer vision on a bigger scale:  Transformer-based architectures: Vision Transformers (ViTs) are increasingly prominent, demonstrating robust efficacy in classification, segmentation, and multimodal applications.  Energy-efficient models: In response to increasing sustainability concerns, researchers are concentrating on pruning, quantisation, and edge deployment to reduce power consumption.  Cross-domain integration: Computer vision is progressively utilised in healthcare, autonomous driving, smart cities, textiles, and sports, necessitating domain-specific modifications.  Ethics and privacy: Striking a balance between innovation and acceptable AI practices is increasingly crucial, particularly in surveillance and biometric applications.  AR/VR integration: The amalgamation of computer vision with augmented and virtual reality facilitates the creation of immersive environments, digital twins, and interactive robotics. The future of computer vision is centred on enhancing systems to be more adaptive, efficient, interpretable, and cognisant of certain domains. Researchers and industry leaders are tackling technical limits and adopting new techniques to develop scalable solutions that can revolutionise several facets of daily living. XI. CONCULSION Computer vision is rapidly maturing into an extremely vibrant and powerful area of Artificial Intelligence, allowing machines to see and understand the world in previously imagined ways only possible for humans. The field has moved forward from classical image processing to contemporary improvements with deep learning and transformer based architectures, continually pushing the frontiers of what technology can achieve. Today, computer vision has moved beyond investigating theoretical possibilities and is profoundly transforming multiple sectors such as health, transport, agriculture, manufacturing, sports and security. These uses demonstrate the versatility of computer vision and its potential to address positive, global and real world problems. International Journal For Technological Research in Engineering Volume 13 Issue 2 October-2025 ISSN (online) 2347-4718 www.ijtre.com Page 40 Although significant advancements have been made, it is a work in progress. The research still faces challenges regarding data imbalance, model interpretability, prohibitive computing costs, and ethical questions regarding privacy and fairness. These issues need to be addressed to ensure the reliability and viability of computer vision systems. Furthermore, the increasing reliance on edge devices and real-time applications calls for light weight and energy efficient models that can maintain high levels of accuracy in constrained environments.The growing integration of computer vision with related fields, such as robotics, natural language processing, and immersive technologies like AR and VR, signifies a hopeful period of interdisciplinary innovation. These collaborations are likely to extend the computer vision domain beyond traditional tasks towards more advanced, interactive, and intelligence systems. To summarize, computer vision has become a transformative force in present-day science and technology. Future advances will not just depend on the development of algorithms and architectures but on the responsible and successful application of these innovations. 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