[1] Liao Ruixuan, Wu Tong, Zhang Yiming, Mao Jianxiao, et al. Vision-based vessel detection for vessel-bridge collision warnings under complex scenes [J]. Journal of Southeast University (English Edition), 2024, 40 (1): 33-40. [doi:10.3969/j.issn.1003-7985.2024.01.004]

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Vision-based vessel detection for vessel-bridge collision warnings under complex scenes()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

40

Issue:

2024 1

Page:

33-40

Research Field:

Traffic and Transportation Engineering

Publishing date:

2024-03-20

Info

Title:

Vision-based vessel detection for vessel-bridge collision warnings under complex scenes

Author(s):

Liao Ruixuan;  Wu Tong;  Zhang Yiming;  Mao Jianxiao;  Wang Hao

Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 211189, China

Keywords:

vessel detection;  vessel-bridge collision;  you-only-look-once version 5(YOLOv5);  squeeze-excitation attention mechanism;  data augmentation

PACS:

U447;U69

DOI:

10.3969/j.issn.1003-7985.2024.01.004

Abstract:

To enable accurate vessel recognition for bridge collision avoidance and early warning, an image dataset for vessels in bridge channels is established using cameras and data augmentation. This dataset includes complex scenarios such as long distances, multiple targets, and low visibility. Subsequently, the you-only-look-once version 5(YOLOv5)model is employed as the basic detector, and several modifications are applied to its network structure. Key enhancements involve replacing C3 modules in the backbone network with C2f modules, integrating the squeeze-excitation attention mechanism into the feature fusion network, and optimizing the prior anchors of the dataset using the K-means++ clustering algorithm. Finally, the modified model undergoes training and validation using PyTorch as the deep learning framework. Results demonstrate that the mean average precision for crucial vessels in the modified YOLOv5 model reaches 99.4%, representing an 11.1% improvement compared to the original YOLOv5 model. Additionally, the inference speed is measured at 102 frame/s. The established YOLOv5 model is a reliable and efficient cornerstone for warning against vessel-bridge collisions in complex navigable scenes.

References:

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Memo

Memo:

Biographies: Liao Ruixuan(1999—), male, Ph. D. candidate; Wang Hao(corresponding author), male, doctor, professor, wanghao1980@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51978155, 52208481).
Citation: Liao Ruixuan, Wu Tong, Zhang Yiming, et al.Vision-based vessel detection for vessel-bridge collision warnings under complex scenes[J].Journal of Southeast University(English Edition), 2024, 40(1):33-40.DOI:10.3969/j.issn.1003-7985.2024.01.004.

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