Hunan Electric Power ›› 2025, Vol. 45 ›› Issue (3): 35-41.doi: 10.3969/j.issn.1008-0198.2025.03.005

• Source and Grid Coordination & Conversion and Utilization • Previous Articles     Next Articles

Underwater Detection Technology of Hydraulic Structure With Multi-Equipment Integration

ZHANG Yike1,2, ZHANG Jun1, MO Jian3, ZHANG Xuewu4, ZHANG Zhen5   

  1. 1. State Grid Hunan Electric Power Company Limited Research institute, Changsha 410208, China;
    2. School of Hydraulic and Environmental Engineering, Changsha University of Science and Technology,Changsha 410114, China;
    3. State Grid Hunan Electric Power Company Limited, Changsha 410004, China;
    4. Hohai University, Nanjing 210000, China;
    5. State Grid Hunan Electric Power Company Limited Hydropower Branch, Changsha 410004, China
  • Received:2025-01-06 Published:2025-07-02

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Abstract: To solve the problem of low accuracy of defect detection in the complex underwater dams environment, a defect detection method based on multi-sensor data fusion is proposed. By combining the sonar equipment and optical camera, the sonar data is denoised by Kalman filter, and the optical image contrast is enhanced by the contrast limited adaptive histogram equalization(CLAHE) algorithm. At the same time, the weighted least square method is used to achieve the fusion of the two kinds of data, and the comprehensive image containing the global contours and high-definition details is generated. Then, convolutional neural network(CNN) is used to process the fusion image to realize accurate identification and classification of defects such as cracks and sediments. The experimental results show that the method successfully detected 3 cracks and 2 sediment accumulation areas in the test dam, significantly improving the accuracy and reliability of defect detection.

Key words: underwater structure, multi-sensor fusion, defect detection, data denoising, convolutional neural network

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