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- Vision-Based Waste Detection for Industrial Sorting LinesPublication . Inácio, Sara Oliveira; Neves, João Carlos Raposo; Proença, Hugo Pedro Martins CarriçoThe increasing production of waste, driven by population growth, has created challenges in managing and recycling materials effectively. Manual waste sorting is a common practice, but it has proven insufficient to handle the large quantities produced and poses health risks to workers involved in the process. The presence of valuable materials in waste streams directed to landfills contributes to soil and water pollution, negatively impacting human health and ecosystems. With advances in deep learning and computer vision technologies, new solutions have emerged to automate and optimize waste sorting processes. In this context, this dissertation proposes a computer vision-based approach for the automatic detection of valuable waste materials in a municipal solid waste (MSW) sorting line. To support this work, a dataset was developed, collected from a Mechanical-Biological Treatment (MBT) facility, comprising eight waste categories. Several state-of-the-art object detection models, including Faster R-CNN, RetinaNet, TridentNet and YOLO, were trained and evaluated. The results demonstrated promising performance, achieving a mAP of 59.7% on the test set. This research and the developed dataset represent a contribution to the application of these technologies in the industrial sector, enhancing worker safety, increasing the efficiency of the recycling process, and supporting the development of sustainable solutions for waste valorization.