WSCA: A floating trash collector system applied deep neural network
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Author
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Huynh Ngoc Thai AnhThe College of Information and Communication Technology, Can Tho University, VietnamTrang Thanh TriThe College of Information and Communication Technology, Can Tho University, Vietnam
Keywords:
WSCA
Deep learning network
Floating trash detection
Yolo v7
Abstract
This article presents an automatic water surface garbage collector using a deep learning model with the YOLOv7 network architecture. First, the Thinking Design method was combined with 3D simulation in the design and evaluation process of the WSCA (Water Surface Cleaning Autobot). The authors then propose a data augmentation method to create the FloW+ dataset, which contains an additional 800 images compared to the FloW dataset (2000 images with 5271 floating plastic waste). Finally, the deep learning model is trained on the FloW and FloW+ datasets to identify trash on water surfaces. The accuracy on the test set in the FloW+ set is a precision of 80.5%, recall of 76.6%, mAP@0.5 of 78.8%, mAP@0.5...95 of 35.6% with an average FPS of 17.6. This method has the potential application for the construction of an autonomous floating trash-collecting robot, as well as scale it up to larger scales.
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Published
Sep 30, 2023
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How to Cite
Huỳnh Ngọc Thái Anh, and Trang Thanh Trí. “WSCA: A Floating Trash Collector System Applied Deep Neural Network”. The University of Danang - Journal of Science and Technology, vol. 21, no. 9.2, Sept. 2023, pp. 47-52, https://jst-ud.vn/jst-ud/article/view/8627.
