A method for detecting floating objects on water based on edge computing

He, Liang; Yang, Shuaipeng; Liu, Jinjiang; Fang, Honglin; Fu, Zhumu; Zhang, Rui; Jia, Huimei; Lv, Lei

doi:10.1109/bmsb58369.2023.10211099

2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) 2023

DOI: 10.1109/bmsb58369.2023.10211099

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A method for detecting floating objects on water based on edge computing

Liang He

Shuaipeng Yang

Jinjiang Liu

et al.

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2023

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Cited by 1 publication

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Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

Li,

Yang,

Zhang

et al. 2023

Water

Self Cite

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Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling is added, and feature fusion is performed using the Feature Pyramid Network. At the same time, bilinear interpolation replaces the up-sampling method to retain feature information and enhance the network’s ability to sense small targets. Network complex algorithms are optimized to better adapt to embedded platforms. Finally, the model is channel pruned to solve the problem of difficult deployment. The experimental results show that this method has a better feature extraction capability as well as a higher detection accuracy. Compared with the original YOLOv5 algorithm, the accuracy is improved by 15.7%, the error detection rate is reduced by 83% in small target task detection, the detection accuracy can reach 92.01% in edge testing, and the inference speed can reach 33 frames per second, which can meet the real-time requirements.

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Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

Li,

Yang,

Zhang

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

show abstract

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A method for detecting floating objects on water based on edge computing

Cited by 1 publication

References 9 publications

Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

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