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ZHANG Hong, JIANG Xiaogang, ZHU Zhiwei, XIA Runchuan, ZHOU Jianting. Review on Intelligent Image Recognition of Apparent Diseases of Stay Cable[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220647
Citation: ZHANG Hong, JIANG Xiaogang, ZHU Zhiwei, XIA Runchuan, ZHOU Jianting. Review on Intelligent Image Recognition of Apparent Diseases of Stay Cable[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220647

Review on Intelligent Image Recognition of Apparent Diseases of Stay Cable

doi: 10.3969/j.issn.0258-2724.20220647
  • Received Date: 27 Sep 2022
  • Rev Recd Date: 11 Jan 2023
  • Available Online: 04 Jun 2024
  • The stay cable is one of the main load-bearing elements of cable-stayed bridges, and the disease of its outer sheath is easy to penetrate inside the cable and affect the health of the steel wire. Therefore, it is significant to use the video image method to intelligently identify the apparent disease of the cable. Based on image recognition, the methods of apparent disease recognition for stay cable were systematically reviewed from two aspects: traditional image detection and deep learning. The basic principles and application effects of each method were introduced, and the current detection examples were analyzed. Some cutting-edge deep learning methods were introduced to provide a reference for the apparent detection of cables. The main features of various methods were summarized, and the problems existing in the current detection were discussed and prospected. The deep learning model-based image recognition method had better recognition accuracy and algorithm robustness, stronger learning ability and adaptability, and optimal comprehensive image defect recognition effect, but there were still difficulties such as the difficult balance between detection accuracy and speed, large image data demand, and high labeling cost. To this end, detection methods could be improved by improving image quality, constructing more semi-supervised and unsupervised deep learning models, and enhancing the learning ability of detection models.

     

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