Waste-YOLO: towards high accuracy real-time abnormal waste detection in waste-to-energy power plant for production safety

Abstract Due to the danger of explosive, oversize and poison-induced abnormal waste and the complex conditions in waste-to-energy power plants (WtEPPs), the manual inspection and existing waste detection algorithms are incapable to meet the requirement of both high accuracy and efficiency. To address the issues, we propose the Waste-YOLO framework by introducing the coordinate attention, convolutional block attention module, content-aware reassembly of features, improved bidirectional feature pyramid network and SCYLLA- intersection over union loss function based on YOLOv5s for high accuracy real-time abnormal waste detection. Through video acquisition, frame-splitting, manual annotation and data augmentation, we develop an abnormal waste image dataset with the four most common types (i.e. gas cans, mattresses, wood and iron sheets) to evaluate the proposed Waste-YOLO. Extensive experimental results demonstrate the superiority of Waste-YOLO to several state-of-the-art algorithms in waste detection effectiveness and efficiency to ensure production safety in WtEPPs.