WANG Yudong, DAI Wei, MA Xiaoping. Rapid detection method of bolt abnormality based on machine visio[J]. Journal of Mine Automation, 2021, 47(4): 13-18. DOI: 10.13272/j.issn.1671-251x.2021020038
Citation: WANG Yudong, DAI Wei, MA Xiaoping. Rapid detection method of bolt abnormality based on machine visio[J]. Journal of Mine Automation, 2021, 47(4): 13-18. DOI: 10.13272/j.issn.1671-251x.2021020038

Rapid detection method of bolt abnormality based on machine visio

More Information
  • The existing manual detection method of bolt abnormality can only perform random inspection on a single bolt, cannot check the bolt abnormality comprehensively, and has low efficiency. When the bolt is abnormal, the exposed section of the bolt often changes in length or angle, or even falls off. According to the characteristics that the length and angle of the exposed section change when the bolt is abnormal, and taking the roadway inspection robot as a platform, a non-contact bolt abnormality detection method consisting of bolt image matching and extraction and bolt characteristic detection is designed based on machine vision technology. In the bolt image matching and extraction stage, perceptual hash algorithm is used to match the collected image with the original image, histogram equalization is used to achieve image enhancement, and YOLOv3 algorithm is used to locate and extract the bolt area. In the bolt characteristic detection stage, bilateral filtering and Canny edge detection algorithm are used to extract bolt image edge information, and line segment detection algorithm is used to extract straight line segments of bolt images. Combined with the characteristic that bolt contour can be regarded as a group of parallel lines, the method can achieve the length and angle characteristic extraction, and compare with the original image bolt characteristics to realize abnormality detection. The laboratory-made data set is used to conduct experiments on the rapid bolt abnormality detection method, and the results shows that the method can detect bolt abnormality quickly and accurately.
  • Related Articles

    [1]LIU Yi, PANG Dawei, TIAN Yu. Multi object personnel detection and dynamic tracking method based on improved KCF[J]. Journal of Mine Automation, 2023, 49(11): 129-137. DOI: 10.13272/j.issn.1671-251x.2023080015
    [2]MU Qi, HAN Jiajia, ZHANG Han, LI Zhanli. A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration[J]. Journal of Mine Automation, 2023, 49(4): 50-61. DOI: 10.13272/j.issn.1671-251x.2022100093
    [3]ZHOU Mengran, LI Xuesong, ZHU Ziwei, HUANG Kaiwen. A joint algorithm of multi-target detection and tracking for underground miners[J]. Journal of Mine Automation, 2022, 48(10): 40-47. DOI: 10.13272/j.issn.1671-251x.2022060040
    [4]ZHANG Xuhui, YAN Jianxing, ZHANG Chao, WAN Jicheng, WANG Lixin, HU Chengjun, WANG Li, WANG Dong. Coal block abnormal behavior identification based on improved YOLOv5s + DeepSORT[J]. Journal of Mine Automation, 2022, 48(6): 77-86, 117. DOI: 10.13272/j.issn.1671-251x.17915
    [5]HAO Jianhua. Research of personnel tracking algorithm for coal mine substation based on CamShift and particle filter[J]. Journal of Mine Automation, 2015, 41(11): 35-38. DOI: 10.13272/j.issn.1671-251x.2015.11.009
    [6]CHENG Deqiang, LIU Jie, GUO Zheng. An algorithm for moving targets tracking in coal mine underground based on layered optical flow[J]. Journal of Mine Automation, 2015, 41(3): 75-79. DOI: 10.13272/j.issn.1671-251x.2015.03.019
    [7]WANG Hai-quan, LIAO Lei, LI Hong-jun, WANG Dong-yu. Design of real-time control system of DC motor based on xPC Target[J]. Journal of Mine Automation, 2013, 39(5): 76-79.
    [8]ZHAI Nai-jiang, LI Cheng-dong. Personnel Tracking Method of Coal Preparation Plant Based on Improved MeanShift Algorithm[J]. Journal of Mine Automation, 2012, 38(2): 32-35.
    [9]NI Qi, YIN Gang. Design of Moving Object Tracking System Based on Background Subtraction Algorithm and CAMShift Algorithm and Its Implementatio[J]. Journal of Mine Automation, 2010, 36(12): 30-32.
    [10]LI Jin-liang, SUN You-xia, BAO Ji-hua, ZHANG Yuan, JIANG Xue. Research of Target Tracking Control of Rescue Robot[J]. Journal of Mine Automation, 2009, 35(12): 22-25.

Catalog

    Article Metrics

    Article views (166) PDF downloads (22) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return