Yangtze River inland waterway bulk carrier sealing status recognition on improved YOLOv5 algorithm

Zhexuan Xiong; Hao Zhang; Yingjie Xiao; Keping Guan

doi:10.1117/12.3015681

20 February 2024 Yangtze River inland waterway bulk carrier sealing status recognition on improved YOLOv5 algorithm

Zhexuan Xiong, Hao Zhang, Yingjie Xiao, Keping Guan

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130642S (2024) https://doi.org/10.1117/12.3015681
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

In order to further respond to the national policy and improve the monitoring capability of the sealing status of inland bulk carriers on the Yangtze River, this paper proposes the C-YOLOv5 model on the basis of the YOLOv5 algorithm. The elkan K-Means clustering algorithm is introduced to optimize the target candidate frame so that it can adapt to the detection environment of small targets while ensuring the recall and accuracy of recognition. In order to improve the focus on the detected targets, the SE focus mechanism module is introduced in the head of the model. In addition, some of the ordinary convolutions in the structure are replaced with depth-separable convolutions to further improve the detection speed. The experimental results show that the detection accuracy and speed are improved by using the C-YOLOv5 model, with an accuracy as high as 91.1% and a speed as high as 66.5 f/s. The detection accuracy and retrieval rate of the C-YOLOv5 algorithm both reach 90%. The C-YOLOv5 algorithm's ability to recognize the target state is also significantly improved under poor line-of-sight and dark light test conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhexuan Xiong, Hao Zhang, Yingjie Xiao, and Keping Guan "Yangtze River inland waterway bulk carrier sealing status recognition on improved YOLOv5 algorithm", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130642S (20 February 2024); https://doi.org/10.1117/12.3015681

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KEYWORDS

Detection and tracking algorithms

Target detection

Convolution

Education and training

Target recognition

Data modeling

Convolutional neural networks

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