期刊论文

Wang, LH

英文

Measurement Science And Technology

0957-0233

2024

35

1

016001

10.1088/1361-6501/ad042a

This work was partly supported by the National Key Research and Development Program of China (Grant 2019YFE0105300), the National Natural Science Foundation of China (Grants 62377010, 62273139, 62171184 and 62106072), the Key Research Foundation of Educati [2019YFE0105300]; National Key Research and Development Program of China [62377010, 62273139, 62171184, 62106072]; National Natural Science Foundation of China [22A0021]; Key Research Foundation of Education Bureau of Hunan Province [2020GK2020]; Science and Technology Innovation Program of Hunan Province

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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 ...