Research on small object detection of waterborne debris based on lightweight algorithms

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Research on small object detection of waterborne debris based on lightweight algorithms
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                        Xu JindaXu Jinda
1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University， Yichang 443002, China； 2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University，Yichang 443002, China； 3.College of Computer and information Technology, China Three Gorges University，Yichang 443002, China
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Chen CifaChen Cifa
2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University，Yichang 443002, China； 3.College of Computer and information Technology, China Three Gorges University，Yichang 443002, China； 4.Big Data Research Center, Jingchu University of Technology，Jingmen 448000, China
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Zhang ShangZhang Shang
1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University， Yichang 443002, China； 2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University，Yichang 443002, China； 3.College of Computer and information Technology, China Three Gorges University，Yichang 443002, China
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Affiliation:1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University， Yichang 443002, China; 2.Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University，Yichang 443002, China; 3.College of Computer and information Technology, China Three Gorges University，Yichang 443002, China; 4.Big Data Research Center, Jingchu University of Technology，Jingmen 448000, China
Clc Number:TP391.41; TN40
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Abstract:

To address the high proportion of small target objects in waterborne debris detection, the interference caused by multiple factors such as water surface fluctuations and shoreline reflections, and the high demands on device performance due to the large number of parameters and computational load of detection models, we propose a lightweight, high-precision, real-time detection model, LS-YOLO. First, this algorithm uses the HS-FPN pyramid network design to construct the Neck network structure of YOLOv8. The constructed network structure sacrifices a small part of the accuracy and significantly reduces the number of parameters and computational complexity of the model. Secondly, HS-FPN is improved by introducing the CAA context-anchored attention mechanism to capture remote contextual information to improve detection accuracy. Then, by replacing the loss function with Wise-IoUv3, which features a dynamic focusing mechanism, the detection performance is significantly improved, increasing the robustness of the model. Finally, LAMP pruning technology is used to prune the model to reduce the number of parameters and calculations of the model. The experiment shows that the improved LS-YOLO has a 0.9% increase in mAP50 compared to the baseline model, a 3.2% increase in recall, a reduction in parameters to 19.83% of the baseline model, a reduction in computational cost to 44.44%, and a reduction in model size to 22.22%. The optimized detection algorithm not only significantly improves detection performance and feature extraction accuracy, but also facilitates deployment on resource-constrained hardware platforms.

Key words:water garbage detection;lightweight;small target;loss function;channel pruning

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Online: December 20,2024
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