Automatic detection of fastener safety wire twisting direction based on machine vision

Jingjing Liu; Shiwei Zhao; Fangyu Hu

doi:10.1117/12.2660005

3 February 2023 Automatic detection of fastener safety wire twisting direction based on machine vision

Jingjing Liu, Shiwei Zhao, Fangyu Hu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111P (2023) https://doi.org/10.1117/12.2660005
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Safety wire status detection is of great significance to aircraft operation safety. To address the problems of low efficiency and leakage in the current manual-led safety wire twisting direction inspection, we propose a machine vision-based safety wire twisting direction detection method for aviation fasteners. Firstly, the Yolov4-MSC objective detection algorithm is proposed for fastener and safety wire region localization. After segmentation of the region localization, we pinpoint the precise location of the fastener by the gradient Hough transform method, the safety wire braiding is detected using the Maximally Stable Extremal Regions (MSER) with Non-Maximum Suppression (NMS), and the safety wire centerline location is fitted using the Random Sample Consensus (RANSAC) algorithm. Lastly, the safety wire twisting direction detection is obtained by the position relationship between the safety wire centerline and the fastener. The experimental results show that the proposed safety wire twisting direction method is effective, has high accuracy and robustness, and can achieve fastener and safety wire location and safety wire twisting direction detection in complex aircraft maintenance scenarios.

Citation Download Citation

Jingjing Liu, Shiwei Zhao, and Fangyu Hu "Automatic detection of fastener safety wire twisting direction based on machine vision", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111P (3 February 2023); https://doi.org/10.1117/12.2660005

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