塑料废料
集合预报
入侵
计算机科学
环境科学
人工智能
工程类
地质学
废物管理
地球化学
作者
Lanfa Liu,Bai-Tao Zhou,Guiwei Liu,Lian Duan,Rongchun Zhang
标识
DOI:10.1109/igarss46834.2022.9883308
摘要
A rapidly increasing amount of plastic waste not only cause serious environmental issues but also pose a considerable threat to the rail transportation. It is important to monitor the intrusion of floating plastics into the railway area. In this article, we propose to detect plastic waste using You Only Look Once-v5 (YOLO-v5) algorithm and model ensemble through surveillance cameras installed along railway lines. Experiments on the size of YOLO-v5 model were carried out to find the optimal size to detect plastics. The model with large size (YOLOv51) outperformed with an overall accuracy (OA) of 82.6% and mean Average Precision (mAP) of 0.822. Two ensemble modelling strategies were implemented considering different size combination of YOLO-v5 models including 1) nano, small and medium sizes; 2) nano, small, medium and large sizes. The latter one achieved the best result with the OA equal to 85.4% and the mAP equal to 0.834. The results indicate that YOLO-based ensemble model can effectively improve the performance of detection plastic waste using surveillance cameras and the acquired knowledge has great potential to UAV- and satellite-based high-resolution imagery.
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