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Real-Time Maritime Obstacle Detection Based on YOLOv5 for Autonomous Berthing

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1566))

Abstract

The shipping industry is developing towards intelligence, in which autonomous berthing and docking systems play an important role. Real-time maritime obstacle detection is essential for autonomous berthing, which can protect ships from collisions. In the field of real-time objection detection, YOLOv5s is one of the most effective networks. To solve the real-time maritime obstacle detection for autonomous berthing, we propose an improved network, named YOLOv5s-CBAM, which employs the convolutional block attention module to boost the representation power of YOLOv5s. In addition, we propose another network, named YOLOv5-SE, in which a squeeze-and-excitation block is introduced to recalibrate the channel-wise features adaptively. The experimental results demonstrate that YOLOv5s-CBAM outperforms YOLOv5s in the detection of large obstacles. Both YOLOv5s-CBAM and YOLOv5s-SE achieve state-of-the-art performance in various metrics compared with previous related work for maritime obstacle detection.

Supported by Jiangsu Key Laboratory of Green Ship Technology (No. 2019Z04).

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Authors and Affiliations

  1. Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Guotong Chen

  2. China Ship Scientific Research Center, No. 222 Shanshui East Road, Binhu District, Wuxi, 214082, China

    Jiangtao Qi & Zeyu Dai

Authors
  1. Guotong Chen
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  2. Jiangtao Qi
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  3. Zeyu Dai
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Corresponding author

Correspondence to Guotong Chen .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Chen, G., Qi, J., Dai, Z. (2022). Real-Time Maritime Obstacle Detection Based on YOLOv5 for Autonomous Berthing. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_32

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  • DOI: https://doi.org/10.1007/978-981-19-1253-5_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1252-8

  • Online ISBN: 978-981-19-1253-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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