DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection

In a global context, the production of urban solid waste significantly varies with changes in living standards. This trend exhibits diversity across different countries and regions, reflecting shifts in lifestyles as well as varying needs and challenges in waste management strategies. However, current standards of waste recycling are too complex for the general public to follow. In this study, we propose a model called DSYOLO-Trash to identify solid waste by integrating the dual attention mechanisms convolutional block attention module (CBAM) and Contextual Transformer Networks(CotNet), which significantly enhance its ability to mine channel-related and spatial attention features while optimizing the learning process. We apply the deep simple online and realtime tracking (DeepSORT) object tracking algorithm to solid waste detection for the first time in the literature to enable the real-time identification and tracking of waste. We also develop a multi-label dataset of mixed solid waste, called MMTrash, to realistically simulate actual scenarios of waste classification. Our proposed DSYOLO-Trash delivered superior performance to classical detection algorithms on both the MMTrash and the TrashNet datasets. Our system combines the improved you only look once(YOLO) algorithm with DeepSORT technology by using industrial cameras and PLC-controlled robotic arms to intelligently sort waste. The work here constitutes an important contribution to intelligent waste management and the sustainable development of cities.