Enhancing recyclable waste classification based on deep residual learning

This study aims to leverage advanced deep learning technologies to facilitate high-accuracy waste classification on mobile devices. We developed a deep neural network model based on ResNet-34, specifically designed for the automatic identification and classification of recyclable waste images. Utilizing a residual learning framework, this model enhances the representational capacity of feature maps, effectively captures deeper features, and maintains information flow, thus preventing the common issue of feature degradation during deep network training. Testing on the TrashNet dataset demonstrated that this model surpasses other common convolutional neural network architectures in multiple performance metrics, including accuracy, precision, recall, and F1 score, achieving a classification accuracy of 86.25% and confirming its efficacy in handling complex waste classification tasks.