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Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm

Author

Listed:
  • Jiaowei Shi

    (Weapon Engineering College, Naval University of Engineering, Wuhan 430034, China)

  • Shiyan Sun

    (Weapon Engineering College, Naval University of Engineering, Wuhan 430034, China)

  • Zhangsong Shi

    (Weapon Engineering College, Naval University of Engineering, Wuhan 430034, China)

  • Chaobing Zheng

    (School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Bo She

    (Weapon Engineering College, Naval University of Engineering, Wuhan 430034, China)

Abstract

For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point.

Suggested Citation

  • Jiaowei Shi & Shiyan Sun & Zhangsong Shi & Chaobing Zheng & Bo She, 2022. "Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15329-:d:976759
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    References listed on IDEAS

    as
    1. Hong Huang & Dechao Sun & Renfang Wang & Chun Zhu & Bangquan Liu, 2020. "Ship Target Detection Based on Improved YOLO Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, August.
    2. Zhijian Huang & Bowen Sui & Jiayi Wen & Guohe Jiang, 2020. "An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network," Complexity, Hindawi, vol. 2020, pages 1-11, April.
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