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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100473


A modified YOLOv4 detection method for a vision-based Underwater garbage cleaning robot


Author(s):  Manjun TIAN, Xiali LI, Shihan KONG, Licheng WU, Junzhi YU

Affiliation(s):  First Research Institute of The Ministry of Public Security of PRC, Beijing 100048, China School of Information Engineering, Minzu University of China, Beijing 100081, China; more

Corresponding email(s):   tianmanjun2018@163.com, xiaer_li@163.com, kongshihan@pku.edu.cn, wulicheng@tsinghua.edu.cn, junzhi.yu@ia.ac.cn

Key Words:  Object detection, Aquatic environment, Garbage cleaning robot, Modified YOLOv4


Manjun TIAN, Xiali LI, Shihan KONG, Licheng WU, Junzhi YU. A modified YOLOv4 detection method for a vision-based Underwater garbage cleaning robot[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100473"
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Abstract: 
To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. This article proposes a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. More specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 FPS with a mAP of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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