|Table of Contents|

[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()
用于船桥碰撞预警的复杂场景下视觉船舶检测
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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
用于船桥碰撞预警的复杂场景下视觉船舶检测
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
廖睿轩;  吴同;  张一鸣;  茅建校;  王浩
东南大学混凝土及预应力混凝土结构教育部重点实验室, 南京 211189
Keywords:
vessel detection;  vessel-bridge collision;  you-only-look-once version 5(YOLOv5);  squeeze-excitation attention mechanism;  data augmentation
船舶检测;  船桥相撞;  YOLOv5;  SE注意力机制;  数据增强
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.
为准确识别航道船舶, 实现桥梁防撞预警, 结合实拍图像和数据增强建立了针对桥梁航道船舶的图像数据集, 包括远距离、多目标以及可视度低等复杂场景.然后, 以YOLOv5模型作为基本检测器并对其网络结构进行改进, 主要改进包括将主干网络中的C3模块替换为C2f模块, 在特征融合网络中嵌入SE注意力机制, 采用K-means++聚类算法对数据集的先验框进行优化.最后, 以PyTorch为深度学习框架对改进YOLOv5模型进行训练和验证.结果表明, 改进YOLOv5模型对重点检测船舶的平均精度达到99.4%, 较原始YOLOv5模型提高了11.1%, 检测速度达到102帧/s, 可为复杂通航场景下船桥碰撞预警提供可靠、高效的支撑.

References:

[1] Yang Y D, Wang X F, Pan J J. Improved CNN and its application in ship identification[J]. Computer Engineering and Design, 2018, 39(10): 3228-3233. DOI:10.16208/j.issn1000-7024.2018.10.039. (in Chinese)
[2] Vagale A, Oucheikh R, Bye R T, et al. Path planning and collision avoidance for autonomous surface vehicles Ⅰ: A review[J]. Journal of Marine Science and Technology, 2021, 26(4): 1292-1306. DOI: 10.1007/s00773-020-00787-6.
[3] Zhang B, Xu Z F, Zhang J, et al. A warning framework for avoiding vessel-bridge and vessel-vessel collisions based on generative adversarial and dual-task networks[J].Computer-Aided Civil and Infrastructure Engineering, 2022, 37(5): 629-649. DOI: 10.1111/mice.12757.
[4] Cui Z Y, Wang X Y, Liu N Y, et al. Ship detection in large-scale SAR images via spatial shuffle-group enhance attention[J].IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 379-391. DOI: 10.1109/TGRS.2020.2997200.
[5] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
[6] Liu W, Anguelov D, Erhan D, et al. SSD: Single shotmultibox detector[C]//European Conference on Computer Vision. Berlin, Germany, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2.
[7] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, NV, USA, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[8] Shao Z F, Wu W J, Wang Z Y, et al.SeaShips: A large-scale precisely annotated dataset for ship detection[J]. IEEE Transactions on Multimedia, 2018, 20(10): 2593-2604. DOI: 10.1109/TMM.2018.2865686.
[9] Li H, Deng L B, Yang C, et al. Enhanced YOLOv3 tiny network for real-time ship detection from visual image[J].IEEE Access, 2021, 9: 16692-16706. DOI: 10.1109/ACCESS.2021.3053956.
[10] Lee W J, Roh M I, Lee H W, et al. Detection and tracking for the awareness of surroundings of a ship based on deep learning[J]. Journal of Computational Design and Engineering, 2021, 8(5): 1407-1430. DOI: 10.1093/jcde/qwab053.
[11] Ni Y H, Mao J X, Wang H, et al. Toward high-precision crack detection in concrete bridges using deep learning[J].Journal of Performance of Constructed Facilities, 2023, 37(3): 04023017. DOI: 10.1061/jpcfev.cfeng-4275.
[12] Zhou J C, Jiang P, Zou A R, et al. Ship target detection algorithm based on improved YOLOv5[J]. Journal of Marine Science and Engineering, 2021, 9(8): 908-922. DOI: 10.3390/jmse9080908.
[13] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 7132-7141. DOI: 10.1109/CVPR.2018.00745.
[14] Xia Y, Chen L M, Wang J J, et al. Single shot multibox detector based vessel detection method and application for active anti-collision monitoring[J]. Journal of Hunan University: Natural Science, 2020, 47(3): 97-105. DOI:10.16339/j.cnki.hdxbzkb.2020.03.012. (in Chinese)
[15] Ni Y H, Lu H, Ji C, et al. Comparative analysis on bridge corrosion damage detection based on semantic segmentation [J]. Journal of Southeast University(Natural Science Edition), 2023, 53(2): 201-209. DOI:10.3969/j.issn.1001-0505.2023.02.003. (in Chinese)

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.
Last Update: 2024-03-20