Detection of Floating Garbage on Water Surface Based on PC-Net

Ning Li, He Huang, Xueyuan Wang, Baohua Yuan, Yi Liu and Shoukun Xu ()
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Ning Li: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China
He Huang: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China
Xueyuan Wang: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China
Baohua Yuan: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China
Yi Liu: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China
Shoukun Xu: School of Computer Science and AI Aliyun School of Big Data & School of Software, Changzhou University, Changzhou 213164, China

Sustainability, 2022, vol. 14, issue 18, 1-16

Abstract: In the detection of surface floating garbage, the existence of complex backgrounds and the small target sizes make the surface floating garbage easy to mis-detect. Existing approaches cannot yet provide a solution to the aforementioned problems and they are typically limited to addressing specific issues. This paper proposes a PC-Net algorithm for floating garbage detection. First, a pyramid anchor generation approach is proposed, which makes the anchor to be generated centrally near the target and reduces the interference of background information in the anchor generation. Then, in the RoI Pooling feature map import stage, the classification map is used as the feature map. This approach generates feature maps with a higher resolution and more distinct features, thereby enhancing the feature information of small targets and enhancing the classification accuracy. Experimental results on floating garbage dataset indicate that the average detection accuracy of the proposed approach is 86.4%. Compared with existing detection approaches, such as Faster R-CNN, YOLOv3, YOLOX, and Dynamic R-CNN, the average accuracy of detection is increased by 4.1%, 3.6%, and 2.8%, respectively.

Keywords: object detection; anchor mechanism; classification discrimination diagram; floating garbage (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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