In a paper published in the journal Scientific Reports, researchers explored the importance of efficient waste management in smart cities focused on trash classification and its role in solid waste management by comparing different classification methods using deep learning (DL), particularly convolutional neural networks (CNNs).
Study: CNNs Optimize Waste Management in Smart Cities. Image Credit: franz12/Shutterstock
Leveraging PyTorch and the trashbox dataset, they tested ten models, with ResNext-101 consistently outperforming others. The findings highlight CNNs' potential to improve waste management and propose a federated learning-based framework for further optimization.
Related Work
Past work in trash detection and classification has seen the emergence of innovative approaches leveraging deep neural models trained on diverse datasets. For instance, TrashNet10 and trash annotations in context (TACO11) datasets were developed to address the challenge of trash sorting, with subsequent efforts leading to the creation of the trashbox dataset, offering more images and classes. Various studies have explored machine learning (ML) and DL models for waste classification, achieving notable accuracies.
Transfer learning models, such as residual networks with 50 layers (ResNet-50) and Inception-v3, have been employed with impressive results. Hybrid DL approaches and practical applications like AlphaTrash24 have also improved waste management systems. Ongoing research continues to explore CNN models and augmentation techniques to enhance trash classification methodologies.
CNN Classification Models: Overview
The study discusses several CNN-based classification models commonly employed in literature to classify the trashbox dataset. These models include GoogLeNet (also known as Inception-v1), notable for its "Inception" modules and efficient computational performance; ResNet, which introduced skip connections to ease training of deep neural networks and support hundreds of layers; ResNeXt, an extension of R esNet incorporating parallel transformation paths within network layers; mobile network version 2 (MobileNetV2), designed for embedded systems and mobile devices, with improvements in resource utilization and computational efficiency; and MobileNetV3, offering variants prioritizing either efficiency or accuracy, suitable for diverse processing needs.
Additionally, ShuffleNetV2 is highlighted for its advancements in real-time response capabilities, achieved through channel split operations and feature map reorganization. These models represent diverse architectures catering to various computational requirements and performance objectives in trash classification tasks.
Model Analysis
The results section presents a comprehensive analysis of various deep neural network (DNN) models tested on the trashbox dataset. The experimental setup outlines the utilization of PyTorch for classification tasks and the hardware specifications. ResNeXt-101 and ResNeXt-50 emerge as the top-performing models, consistently achieving high accuracy and F1 scores across training, validation, and test datasets. Despite exhibiting lower training accuracy, ShuffleNetV2 performs similarly, indicating potential overfitting in the ResNeXt models.
Among ResNet models, ResNet-34 performs decently, while MobileNetV2 and MobileNetV3-Large demonstrate relatively good performance but fall short of ResNeXt and ResNet models. Additionally, GoogLeNet achieves competitive performance but slightly lags behind ResNeXt models. The balance between model complexity and performance is emphasized, with ResNeXt50 identified as a well-balanced option considering computational resources and model size.
The analysis further delves into ResNeXt-101's training and validation dynamics, showcasing its effective learning process and generalization capacity. The confusion matrix provides insights into the classification performance across waste classes, highlighting ResNeXt-101's accuracy and misclassification rates. Model complexities are discussed, indicating varying trade-offs between computational resources and model sizes. ResNeXt50 emerges as a balanced choice, offering moderate complexity and adaptable performance.
MobileNet models are highlighted for their lightweight architectures, catering to resource-constrained deployment scenarios. The study emphasizes the importance of carefully considering performance requirements and computational constraints when selecting an appropriate model for trash classification tasks.
Federated Learning Proposal
This proposal advocates for adopting a federated learning framework to enhance the visual detection capabilities of waste management facilities across various environmental conditions. Leveraging the collective competence of multiple ML models, particularly ResNeXt-101, ShuffleNetV2, ResNet-34, and MobileNetV3-Large, the framework aims to address the diverse challenges posed by different types of trash. By deploying these models in a distributed manner, each facility can optimize its detection capabilities locally while benefiting from the collective learning experience shared through the federated server.
In this proposed framework, waste management facilities operate as nodes in a federated network, communicating encrypted updates of model parameters to the central federated server. These parameters, including weights and biases, are aggregated to compute a weighted average, representing the collective wisdom derived from all connected models. By running multiple models in tandem and adapting their parameters through reinforcement learning, each facility can tailor its detection capabilities to specific trash classes, ensuring optimal performance across the entire dataset.
The federated learning approach improves detection accuracy and facilitates knowledge sharing among waste management facilities. The framework fosters a collaborative ecosystem by exchanging learning experiences and refined model parameters, enhancing trash detection's overall efficiency and effectiveness in waste management operations.
Conclusion
To summarize, the study assessed the effectiveness of various DL models in waste detection and classification using the trashbox dataset. ResNeXt-101 and ResNeXt-50 consistently demonstrated superior performance, with ResNeXt-101 achieving the highest accuracy and F1 score.
While ResNet-34 also performed well, MobileNet-based models showed potential limitations due to their lightweight design. Balancing model complexity with performance was crucial, especially considering deployment in smart city waste management systems. Additionally, a federated learning framework proposal suggested integrating multiple models, such as ResNeXt-101, ShuffleNetV2, ResNet-34, and MobileNetV3-Large, to optimize trash classification across various waste management facilities.
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