Convolutional neural network models in municipal solid waste classification: towards sustainable management

Academic Editor: Jian Tang Received: 21 February 2025 Revised: 5 April 2025 Accepted: 8 April 2025 Published: 14 April 2025 Citation: Castro-Bello, M.; Roman-Padilla, D.B.; Morales-Morales, C.; Campos-Francisco, W.; MarmolejoVega, C.V.; Marmolejo-Duarte, C.; Evangelista-Alcocer, Y.; GutiérrezValencia, D.E. Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management. Sustainability 2025,17, 3523. https:// doi.org/10.3390/su17083523 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management Mirna Castro-Bello 1 , Dominic Brian Roman-Padilla 1 , Cornelio Morales-Morales 2, * , Wilfrido Campos-Francisco 1 , Carlos Virgilio Marmolejo-Vega 1, Carlos Marmolejo-Duarte 3, Yanet Evangelista-Alcocer 1 and Diego Esteban Gutiérrez-Valencia 1 1Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico; [email protected] (M.C.-B.); [email protected] (D.B.R.-P.) 2Technological Institute of San Juan del Río, National Institute of Technology of Mexico, San Juan del Rio Queretaro 76800, Mexico 3Center of Land Policy and Valuations, Barcelona School of Architecture (ETSAB), Polytechnic University of Catalonia, 08034 Barcelona, Spain; [email protected] *Correspondence: [email protected] Abstract: Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society. Keywords: object detection; waste; Convolutional Neural Networks; Raspberry Pi; embedded system; sustainable development; management 1. Introduction The management of Municipal Solid Waste (MSW) is a major challenge, due to its high generation rate. In 2020, the total amount generated worldwide was estimated at 2.24 billion tons. This is attributed to rapid population growth and the ongoing advancement of Sustainability 2025,17, 3523 https://doi.org/10.3390/su17083523 Sustainability 2025,17, 3523 2 of 18 urbanization. Projections for the year 2050 indicate a 73% increase compared to the figures reported in 2020, reaching an estimated total of 3.88 billion tons [ 1 ]. The recognition and classification of Municipal Solid Waste (MSW) images offers the opportunity to identify and catalog them using computer vision techniques, optimizing intelligent and effective management processes to maintain urban environment quality, improve citizens’ quality of life, and contribute to achieving sustainable development objectives [ 2 ]. On the other hand, modern object identification technology helps to improve object detection; for example, lightweight deep learning algorithms with reduced size achieve similar accuracy to models that require higher costs and requirements. These have achieved notable advances, standing out for their compact size and higher speed without a decrease in accuracy, and providing theoretical and technical support; they are particularly useful in integrated devices, allowing the achievement of superior levels of classification efficiency [3–5]. Convolutional Neural Networks (CNN) are deep learning models focused on image processing and hierarchical feature extraction, as well as spatial locality modeling. They consist of multiple convolutional and fully connected layers, facilitating the extraction of more detailed features and better image interpretation, and capturing, in turn, more global context information. By understanding the general structure and semantics of each model, its utility can be determined. Some examples of deep network structures are ResNet, Inception, EfficientNet, ShuffleNet, and YOLO [ 2 ]. ResNet-50 is a deep CNN architecture focused on solving problems such as performance degradation, learning difficulty, and convergence problems with increased layers. Applying residual connections to learn from mappings and extract detailed features from images, it consists of convolutional layers, batch normalization, pooling, and fully connected layers [ 6 ]. ShuffleNet v2 focuses on designing more efficient networks by optimizing memory usage and network speed, maintaining the same number of input and output channels of the convolutional layer to minimize memory access cost, reducing the use of group convolutions to save resources, simplifying the network branch structure to improve parallel operation, and decreasing element-level operations to improve network operation [ 7 ]. You Only Look Once (YOLO) is an object detection model designed for real-time image processing. By analyzing an image once while moving, it can detect and classify objects through prior training based on a labeled and cataloged dataset. It outperforms traditional CNNs in terms of efficiency for real-time object detection, by using a single CNN to predict bounding boxes and class probabilities simultaneously. In addition to having fast frame detection, it reasons globally, and performs rapid learning to identify and generalize object characteristics [ 8 ]. According to Ultralytics, YOLO approaches object detection as a single regression problem by predicting bounding boxes and class probabilities from full images in one evaluation. This approach makes YOLO faster than previous two-stage detectors, while still maintaining high precision, and it allows for rapid export, implementation, and execution on Raspberry Pi devices. YOLOv4-tiny, considered a lightweight model designed based on YOLOv4, uses less memory and operates faster, significantly increasing its viability for implementation in smart devices [ 9 ]. YOLOv7-tiny, the lightweight version of YOLOv7, stands out for its speed and efficiency, with 6.2 M parameters and 13.8 B operations (FLOPs). It is especially effective in detecting objects of various sizes, achieving performance scores of 18.8% for small objects, 42.4% for medium objects, and 51.9% for large objects [ 10 ]. YOLOv8n (nano) is a lightweight model designed for speed and efficiency, and is particularly suitable for devices with limited computing resources. It considers only 3.2 M parameters and 8.7 B FLOPs, is optimized to run on a CPU, and its smaller architecture makes it ideal for edge device implementations, while still maintaining good object detection performance [ 10 ]. Finally, YOLOv9t (tiny) is a simplified version of YOLOv9 designed for resource-limited applications, featuring 2.0 M parameters and 7.7 B FLOPs, with an inference time of 2.3 ms Sustainability 2025,17, 3523 3 of 18 on a T4 GPU with TensorRT10. This version is particularly useful for deployment on edge devices and mobile applications where computational resources are limited, without significantly sacrificing precision [10]. Literature Review In the field of object detection models, notable advances have been reported in the literature. For example, the authors of [ 11 ] designed a model called GC-YOLOv5 for classifying batteries, orange peels, used paper, cups, and paper bottles. They used 642 images for training and 40 for validation, achieving an accuracy of 99.86% after 500 epochs, implementing their application with a JavaScript interface. Similarly, the authors of [ 12 ] developed the PublicGarbageNet algorithm with a final accuracy of 96.35%, which classifies four categories: kitchen waste, recyclables, hazardous waste, and other types. It is focused on multitasking and is based on CNN architecture, and was trained with a public dataset of 10,624 images and 10 classes (kitchen waste, plastic, paper, electronics, metal, glass, non-recyclable paper and plastics, textiles, and hazardous waste). Its performance was improved by optimizing the backbone, data augmentation, hyperparameter tuning, and label smoothing. Meanwhile, the authors of [ 13 ] proposed a waste classification algorithm that is resistant to the low accuracy caused by light and shadow interference, with adaptive capability in image lighting, using a threshold replacement method to reduce shadow noise, along with the Canny operator to help crop the white background in images. The algorithm was optimized based on the MLH-CNN model, achieving an accuracy of 96.77% with the authors’ constructed dataset and 93.72% with TrashNet. References [ 14 – 16 ] developed a lightweight CNN model with an efficient algorithm on an embedded device for detecting surface defects in industrial products, reaching accuracy levels comparable to those of state-of-the-art (SOTA) models, while using fewer parameters and requiring fewer computations. Additionally, the authors implemented safe and optimal fault-tolerant control, based on neural networks, to reconstruct system dynamics from data, designing an adaptive critical scheme with a non-quadratic cost function to handle input constraints and reduce computational load. With regard to datasets, the authors of [ 2 ] developed an integrated method of waste image recognition and classification that combines ResNet-50, YOLOv5, and weakly supervised CNN algorithms, improving the accuracy and efficiency of image recognition. ResNet-50 was used to extract features from the images, and weakly supervised CNNs were used for training and prediction. The authors evaluated four public datasets: HGI-30, TrashNet, GINI, and Public GarbageNet. With HGI-30, the inference time, FLOPs, and MAPE were reduced by more than 48.6%, 46.5%, and 41%, respectively, and an accuracy of 97.02% was achieved. Meanwhile, the authors of [ 17 ] presented a critical analysis of 11 existing waste datasets, collecting and summarizing previous studies by each author and their experimental results. Additionally, they designed two benchmark datasets, combining detect-waste, classify-waste, and all the categories identified in the study. Finally, they presented a two-stage detector for waste classification and localization, using EfficientDetD2 to locate waste and EfficientNet-B2 to classify it through semi-supervised training using unlabeled images, achieving 70% average precision in waste localization and 75% in classification. Another factor to consider is the comparative performance of models that can be applied in embedded systems. For example, the authors of [ 18 ] presented a model to classify inorganic containers in waste processing sites. They compared five CNN architectures: Xception, Inception V3, ResNet-50, ResNet-50 V2, and DenseNet-201, previously trained with a dataset of HDPE plastic, PET, glass bottles and jars, cans, cardboard, and flexible plastic containers, collected from three different Kaggle resources: a water bottle image Sustainability 2025,17, 3523 4 of 18 dataset, waste classification, and waste classification into 12 classes. For training, they considered data augmentation, rescaling, rotation, displacement, tilting, zoom, and flipping, obtaining a CNN model based on the DenseNet-201 architecture with an accuracy of 0.96%. Under this same approach, the contributions of [ 6 ] focused on creating a deep learningbased waste classification system that enables recognition and recycling of household waste through a lightweight garbage classification model, GCNet (Garbage Classification Network). The ShuffleNet v2 model was improved by integrating a parallel mixed attention mechanism (PMAM), new activation functions, and transfer learning, achieving an average accuracy of 97.9%, and it was applied on a Raspberry Pi 4B, with an inference time of 105 ms. The system completed the classification and collection of a single object in 0.88 s. Similarly, in [ 19 ], an intelligent waste classification system based on GNet deep learning was generated through Linux and applied on a Raspberry Pi 4B as the main board, in addition to the integrated hardware of a two-degrees-of-freedom servo, touch panel, and camera. The model was trained with the Huawei Garbage Classification Challenge Cup dataset, obtaining an accuracy of 92.62% and an efficiency of 0.63 s, complemented by the development of a graphical user interface based on Python 3.6.8 and QT. Finally, the authors of [ 20 ] applied a model in TensorFlow Lite v2.13 on a Raspberry Pi 4 for real-time waste classification and categorization. The servomotors were mounted on a plastic board that separated the waste according to the model’s classification and deposited it in appropriate compartments depending on its type. The present study analyzes the effectiveness of four CNN models in MSW detection, using a Raspberry Pi 4 Model B. YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9tiny models were trained and evaluated, with their performance compared in terms of precision, inference speed, and resource usage in an embedded system. A dataset was adjusted with 85% of images for training and 15% for testing. In the experiments conducted, learning rate, weight decay, epochs, and online training were configured, generating image rotation and mosaics, and the behaviors of model precision and loss were observed. The comparison of the models allowed for identification of the most suitable one for real-time MSW detection, considering hardware limitations and the need to maintain a balance between efficiency and precision in a low-cost and low-energy-consumption environment. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Their application in an embedded system with an HQ camera obtained an average of 93% CPU usage and an inference time of 1800 ms. 2. Materials and Methods 2.1. Materials For this research, a Dell G15 5530 laptop with CPU i7 13650HX, GPU RTX 4060 8GB VRAM, and 32 GB RAM, a Raspberry Pi 4 model B with an HQ camera, Roboflow tool [ 21 ], YOLOv4 tiny [ 22 ], YOLOv7 tiny [ 23 ], YOLOv8 nano [ 24 ] and YOLOv9 nano [ 25 ] were employed. 2.2. Methodology To evaluate and compare the effectiveness of four convolutional neural network models for MSW detection, a four-phase methodology was developed, as shown in Figure 1. Sustainability 2025,17, 3523 5 of 18 Sustainability 2025, 17, x FOR PEER REVIEW 5 of 18 Figure 1. Methodological diagram. 2.2.1. Dataset Design The design of the dataset of 1883 images involved labeling based on a comparative analysis evaluating the types of waste, as well as image characteristics such as resolution, white backgrounds without irregular edges, uncontrolled backgrounds in green areas, and waste combinations. In addition, several different datasets, including Garbage Classification and Trashnet, were used, which were classified and balanced into inorganic, with 997 images (53%), and organic, with 886 images (47%). For labeling, the Roboflow tool was used, manually delimiting the areas of interest for the objects to be detected. Subsequently, image preprocessing methods were applied to resize the images to 640 × 640 pixels and perform automatic contrast adjustments. Additionally, data augmentation techniques were implemented, rotating the images at angles of +15° and −15°, and applying horizontal and vertical flips to expand the diversity of the dataset, (Table 1, Figure 2). Table 1. Collected datasets. Name No. of Categories No. of Images Annotation Image Type Author UAVVaste 1 (garbage waste in green areas) 772 Segmentation Waste capture in open fields [25] Trashnet 6 (glass, paper, cardboard, plastic, metal, general waste) 2527 Classification Clean background or white background [26] Waste Classification data 2 (organic and recyclable objects) ~25,000 Classification Generated by Google searches [27] Garbage Classification 12 (battery, biological, brown glass, cardb oard, clothes, green glass, metal, paper, plastic, shoes, trash, white glass) 15,150 Classification/Detection Combining the “clothing dataset” and the web scrapping tool [28] TrashBox 7 (medical waste, electronic waste, plastics, paper, metal, glass, cardboard) 17,785 Classification/Detection Generated by the web [29] Figure 1. Methodological diagram. 2.2.1. Dataset Design The design of the dataset of 1883 images involved labeling based on a comparative analysis evaluating the types of waste, as well as image characteristics such as resolution, white backgrounds without irregular edges, uncontrolled backgrounds in green areas, and waste combinations. In addition, several different datasets, including Garbage Classification and Trashnet, were used, which were classified and balanced into inorganic, with 997 images (53%), and organic, with 886 images (47%). For labeling, the Roboflow tool was used, manually delimiting the areas of interest for the objects to be detected. Subsequently, image preprocessing methods were applied to resize the images to 640 × 640 pixels and perform automatic contrast adjustments. Additionally, data augmentation techniques were implemented, rotating the images at angles of +15 ◦ and − 15 ◦ , and applying horizontal and vertical flips to expand the diversity of the dataset, (Table 1, Figure 2). Table 1. Collected datasets. Name No. of Categories No. of Images Annotation Image Type Author UAVVaste 1 (garbage waste in green areas) 772 Segmentation Waste capture in open fields [26] Trashnet 6 (glass, paper, cardboard, plastic, metal, general waste) 2527 Classification Clean background or white background [27] Waste Classification data 2 (organic and recyclable objects) ~25,000 Classification Generated by Google searches [28] Garbage Classification 12 (battery, biological, brown glass, cardboard, clothes, green glass, metal, paper, plastic, shoes, trash, white glass) 15,150 Classification/ Detection Combining the “clothing dataset” and the web scrapping tool [29] TrashBox 7 (medical waste, electronic waste, plastics, paper, metal, glass, cardboard) 17,785 Classification/ Detection Generated by the web [30] Sustainability 2025,17, 3523 6 of 18 Sustainability 2025, 17, x FOR PEER REVIEW 6 of 18 Figure 2. Dataset representation built. 2.2.2. YOLO Model Training For training the models, the hyperparameters and the custom dataset of 1883 images were considered, using 85% for training and 15% for testing, along with online data augmentation techniques such as flipud: 0.5, fliplr: 0.5, and mosaic: 1.0 (Table 2). Table 2. Trained models. Model Settings: YOLOv4 Tiny Model Settings: YOLOv7 Tiny Model Settings: YOLOv8 Nano Model Settings: YOLOv9 Tiny Input 640 × 640 Input 640 × 640 Input 640 × 640 Input 640 × 640 Learning rate 0.002 Learning rate 0.001 Learning rate 0.001 Learning rate 0.001 Weight decay 0.0002 Optimizer Adam Optimizer Adam Optimizer Adam Optimizer Adam Momentum 0.937 Momentum 0.937 Momentum 0.937 Momentum 0.937 Batchsize 32 Batchsize 32 Batchsize 32 Batchsize 32 Subdivisions 8 Subdivisions 8 Subdivisions 8 Subdivisions 8 Total epoch 100 Total epoch 100 Total epoch 100 Total iterations 6000 For training YOLO, the input image is divided into a grid of size, where each cell generates a region of interest and predicts the number of bounding boxes, along with its confidence level in each of them, and the number of model classes with their probabilities. The predictions were encoded in a tensor of size S × S (B × 5 + C), as shown in Figure 3 [30], where, S is the number of cells in the image grid; B is the number of bounding boxes; C is the number of model classes; and 5 is the information associated with each bounding box (x, y, w, h, confidence). Figure 2. Dataset representation built. 2.2.2. YOLO Model Training For training the models, the hyperparameters and the custom dataset of 1883 images were considered, using 85% for training and 15% for testing, along with online data augmentation techniques such as flipud: 0.5, fliplr: 0.5, and mosaic: 1.0 (Table 2). Table 2. Trained models. Model Settings: YOLOv4 Tiny Model Settings: YOLOv7 Tiny Model Settings: YOLOv8 Nano Model Settings: YOLOv9 Tiny Input 640 ×640 Input 640 ×640 Input 640 ×640 Input 640 ×640 Learning rate 0.002 Learning rate 0.001 Learning rate 0.001 Learning rate 0.001 Weight decay 0.0002 Optimizer Adam Optimizer Adam Optimizer Adam Optimizer Adam Momentum 0.937 Momentum 0.937 Momentum 0.937 Momentum 0.937 Batchsize 32 Batchsize 32 Batchsize 32 Batchsize 32 Subdivisions 8Subdivisions 8Subdivisions 8 Subdivisions 8Total epoch 100 Total epoch 100 Total epoch 100 Total iterations 6000 For training YOLO, the input image is divided into a grid of size, where each cell generates a region of interest and predicts the number of bounding boxes, along with its confidence level in each of them, and the number of model classes with their probabilities. The predictions were encoded in a tensor of size S × S (B × 5 + C), as shown in Figure 3[ 31 ], where, S is the number of cells in the image grid; B is the number of bounding boxes; C is the number of model classes; and 5 is the information associated with each bounding box (x, y, w, h, confidence). The models were adjusted with their own hyperparameters, and their precision, recall, and F1-Score were calculated (Table 3, Figure 4). Sustainability 2025,17, 3523 7 of 18 Sustainability 2025, 17, x FOR PEER REVIEW 7 of 18 Figure 3. Representation of YOLO model training, adapted from [30]. The models were adjusted with their own hyperparameters, and their precision, recall, and F1-Score were calculated (Table 3, Figure 4). Figure 4. Confusion matrices generated. Figure 3. Representation of YOLO model training, adapted from [31]. Table 3. YOLO model calculations. Model Clase Valores Precision Recall F1-Score IoU YOLOv9 tiny Inorganic TP = 0.97 FN = 0.19 FP = 0.04 0.97 0.97 +0.04 0.96 0.97 0.97 +0.19 0.836 2×0.96 ×0.836 0.96 +0.836 1.605 1.796 0.893 0.97 0.97 +0.19 +0.04 0.97 1.2 0.808 Organic TP = 0.93 FN = 0.04 FP = 0.06 0.93 0.93 +0.06 0.939 0.93 0.93 +0.04 0.958 2×0.939 ×0.958 0.939 +0.958 1.799 1.897 0.948 0.93 0.93 +0.04 +0.06 0.93 1.03 0.903 Validation Metrics To evaluate the performance of the classification or detection model, the numbers of True Positives (TPs), False Positives (FPs), True Negatives (TNs), and False Negatives (FNs) obtained from the confusion matrices generated in Figure 4were considered, and calculated in relation to the following: The Intersection over Union (IoU) Each input image contains bounding boxes (ground-truth); during the learning process, new predicted bounding boxes are generated. When there is an object within the area of interest, the generated bounding box overlaps with the referenced one, calculating the union area with the obtained confidence, obtaining a relationship between the intersection zone and the union area; see Equation (1) and Tables 3and 4[32]. IoU =TP TP +FN +FP (1) Sustainability 2025,17, 3523 8 of 18 Sustainability 2025, 17, x FOR PEER REVIEW 7 of 18 Figure 3. Representation of YOLO model training, adapted from [30]. The models were adjusted with their own hyperparameters, and their precision, recall, and F1-Score were calculated (Table 3, Figure 4). Figure 4. Confusion matrices generated. Figure 4. Confusion matrices generated. Table 4. Metrics obtained from YOLO models. Model Precision Recall F1-Score IoU YOLOv4 tiny 0.91 0.94 0.85 0.60 YOLOv7 tiny 0.91 0.93 0.91 0.83 YOLOv8 nano 0.94 0.97 0.85 0.64 YOLOv9 tiny 0.92 0.94 0.92 0.85 Precision Precision measures the proportion of correct detections among all detections made. A high precision value indicates that the model is making few false detections; see Equation (2) and Tables 3and 4[20]. Precision =TP TP +FP (2) Recall Recall measures the proportion of real objects that are correctly detected. A high recall value indicates that the model is finding most of the objects present in the images; see Equation (3) and Tables 3and 4[33]. Recall =TP TP +FN (3) F1-score According to Ultralytics, this metric allows for the observation of the balance between precision (P) and recall (R), evaluating model performance in a more balanced way, identify- Sustainability 2025,17, 3523 9 of 18 ing whether a model works well across all classes or whether it is biased towards a specific class; see Equation (4) and Tables 3and 4. F1−score =2×P×R P+R(4) Another important aspect to consider in model training is the graphical representation of metrics to visualize the model’s performance during its training (Figure 5). Sustainability 2025, 17, x FOR PEER REVIEW 9 of 18 Table 4. Metrics obtained from YOLO models. Model Precision Recall F1-score IoU YOLOv4 tiny 0.91 0.94 0.85 0.60 YOLOv7 tiny 0.91 0.93 0.91 0.83 YOLOv8 nano 0.94 0.97 0.85 0.64 YOLOv9 tiny 0.92 0.94 0.92 0.85 Another important aspect to consider in model training is the graphical representation of metrics to visualize the model’s performance during its training (Figure 5). Figure 5. Graphs of models’ performance in training. 2.2.3. Embedded System Implementation During implementation of the embedded system, the trained weights of each model were migrated to the Raspberry Pi 4 Model B device, where each one was executed in a Python 3.13.0 language environment in Thonny IDE to start model detection. Experimental tests were conducted with five organic (general food waste) and inorganic (plastics, paper, glass, aluminum) waste items. This allowed for the identification of their behavior in a real environment. YOLOv4 tiny: This detected organic and inorganic residues with difficulty, due to lighting and background problems and instability according to the object’s position, and it had a Central Processing Unit (CPU) usage of around 80% (Figure 6). Figure 5. Graphs of models’ performance in training. 2.2.3. Embedded System Implementation During implementation of the embedded system, the trained weights of each model were migrated to the Raspberry Pi 4 Model B device, where each one was executed in a Python 3.13.0 language environment in Thonny IDE to start model detection. Experimental tests were conducted with five organic (general food waste) and inorganic (plastics, paper, glass, aluminum) waste items. This allowed for the identification of their behavior in a real environment. YOLOv4 tiny: This detected organic and inorganic residues with difficulty, due to lighting and background problems and instability according to the object’s position, and it had a Central Processing Unit (CPU) usage of around 80% (Figure 6). Sustainability 2025,17, 3523 16 of 18 included in the training set. This differs from our study, where we used real-time images with four YOLO models in their most compact versions. Another important aspect is the evaluation using [email protected], where [ 38 ] obtained a maximum average of 0.613 in their YOLOv5m model, which is lower than the results obtained in this research: YOLOv7-tiny = 0.949, YOLOv8-nano = 0.976, and YOLOv9-tiny = 0.968. Additionally, ref. [ 38 ] did not conduct tests on an embedded system. However, refs. [ 14 – 16 ] developed a lightweight CNN model with an efficient algorithm on an embedded device for detecting surface defects in industrial products, achieving accuracy comparable to state-of-the-art models; this aligns with our work on the implementation of lightweight YOLO models in embedded systems. Regarding the dataset, it agrees with [ 2 ], in which Trash-net and Public GarbageNet were also used to train the MSW detection model. It is worth mentioning that [ 2 ] evaluated a single model with four datasets, while in this research, four models were evaluated with a combined dataset of Trash-net and Public GarbageNet. The use of a Raspberry Pi 4B device as the main board for object detection in embedded systems [ 6 ] is consistent with this work. Regarding inference times, in [ 6 ], GCNet, MobileNet v2, and ResNet50 were evaluated, obtaining times of 200 ms, 200 ms, and 600 ms, respectively, which are different from the 1800 ms obtained in this experimentation; however, the models in this study detected more than one type of waste simultaneously, while those in [ 6 ] only detected a single type of waste. 5. Conclusions YOLO versions of object detection models require high-spec devices, without considering resource consumption in their implementation; they are commonly not applied in embedded systems, such as the Raspberry Pi 4 Model B with the 12.3 MP HQ camera, due to their limited CPU and GPU capabilities. Regarding the comparison of the models in their different versions, the performance of object detection models has been investigated. This research presents a comparative analysis of four YOLO models tested under the same environmental conditions and with the same datasets, the YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny versions, to determine each one’s performance in terms of CPU usage, inference time, FPS, precision, recall, [email protected], F1-score, and model size. The results obtained show that YOLOv9-tiny achieved a very solid performance in the experimental tests, with an average precision of 92.11%, a recall of 94.97%, an F1-score of 0.729, a compact size of 4.43 MB, and an absence of errors during testing, positioning it as the best model in terms of stability and overall performance. Meanwhile, YOLOv7 demonstrated high precision in recognizing waste, but also detected non-existent objects, exhibited high CPU usage (97%), showed dependence on the object’s location and lighting, showed weakness in determining distances with organic waste, and had long inference times for object type identification. This research will allow us to evaluate the behavior of lightweight YOLO models applied on a Raspberry Pi 4B device, without relying on remote systems, for implementation, for example, in an intelligent container for classifying organic and inorganic waste, which would ensure proper management and contribute to the Sustainable Development Goals, specifically Goal 13: Climate Action. Author Contributions: Conceptualization, C.M.-M., W.C.-F., C.M.-D., Y.E.-A. and D.E.G.-V.; Methodology, M.C.-B. and C.M.-D.; Software, D.B.R.-P., W.C.-F. and Y.E.-A.; Validation, M.C.-B., D.B.R.-P. and C.M.-D.; Formal analysis, M.C.-B., D.B.R.-P. and D.E.G.-V.; Investigation, M.C.-B., D.B.R.-P., C.M.-M. and C.V.M.-V.; Data curation, D.B.R.-P.; Writing—original draft, M.C.-B. and D.B.R.-P.; Writing— review & editing, M.C.-B., D.B.R.-P., C.V.M.-V. and C.M.-D.; Visualization, D.E.G.-V.; Supervision, M.C.-B. and C.M.-M.; Project administration, C.M.-M. All authors have read and agreed to the published version of the manuscript. Sustainability 2025,17, 3523 17 of 18 Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data associated with the study will be available in the repository of the National Technological Institute of Mexico/Technological Institute of Chilpancingo, https: //rinacional.tecnm.mx/handle/TecNM/149 (accessed on 1 February 2025). The data presented in this study are available upon request from the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Jin, B.; Li, W. Spatial Effects and Driving Factors of Consumption Upgrades on Municipal Solid Waste Eco-Efficiency, Considering Emission Outputs. Sustainability 2025,17, 2356. [CrossRef] 2. Zhou, Y.; Wang, Z.; Zheng, S.; Zhou, L.; Dai, L.; Luo, H.; Zhang, Z.; Sui, M. Optimization of Automated Gar-bage Recognition Model Based on ResNet-50 and Weakly Supervised CNN for Sustainable Urban Development. Alex. Eng. J. 2024,108, 415–427. [CrossRef] 3. De Carolis, B.; Ladogana, F.; Macchiarulo, N. YOLO TrashNet: Garbage Detection in Video Streams. 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