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Lightweight Surface Litter Detection Algorithm Based on Improved YOLOv5s

Zunliang Chen1,2, Chengxu Huang1,2, Lucheng Duan1,2, Baohua Tan1,2,*

1 College of Science (College of Chip Industry), Hubei University of Technology, Wuhan, 430068, China
2 National “111 Research Center” Microelectronics and Integrated Circuits, Hubei University of Technology, Wuhan, 430068, China

* Corresponding Author: Baohua Tan. Email:

Computers, Materials & Continua 2023, 76(1), 1085-1102.


In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce computational effort. Using a Convolutional Block Attention Mechanism (CBAM) module in the backbone network to strengthen the network’s ability to extract significant target features from images. Finally, the loss function is optimized using the Focal-EIoU loss function to improve the convergence speed and model accuracy. The experimental results illustrate that the improved algorithm outperforms the original Yolov5s in all aspects of the homemade water surface litter dataset and has certain advantages over some current mainstream algorithms in terms of model size, detection accuracy, and speed, which can deal with the problems of real-time detection of water surface litter in real life.


Cite This Article

Z. Chen, C. Huang, L. Duan and B. Tan, "Lightweight surface litter detection algorithm based on improved yolov5s," Computers, Materials & Continua, vol. 76, no.1, pp. 1085–1102, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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