Research on visual recognition technology for appearance defects of steel wire rope in mine hoist

A visual recognition method for appearance defects of mine hoist steel wire ropes based on computer vision and deep learning is proposed to address the problems of difficult deployment for detecting multiple steel wire ropes, low image acquisition quality of steel wire ropes, poor adaptability and accuracy of visual detection methods. Firstly, an online monitoring system for the steel wire rope of the mine hoist is constructed. Secondly, the steel wire rope images are collected by the ground mobile inspection platform and the underground intrinsic safety high-speed camera, and a steel wire rope image dataset is established. Considering the effects of underground dust, susceptibility of camera lenses to contamination, uneven lighting, and high light reflection of steel wire ropes, image denoising methods based on Retinex algorithm and homomorphic filtering are used to denoise the steel wire rope images. The processing results show that the automated multi-scale Retinex with color restoration (AutoMSRCR) algorithm based on color gain weighting is the optimal solution. The defect detection process is based on convolutional neural networks, and a defect detection model based on YOLOv5s is constructed. In order to reduce the influence of human factors and the workload of parameter tuning, a Focus structure is added to YOLOv5s for optimization. The improved YOLOv5s model is used as a pre training model for steel wire rope defect detection to further reduce the memory usage of the model and improve the loading and detection speed of the model. The experimental results show that the proposed method has detection errors of 1.61% and 1.35% for wire breakage at 2 positions of the steel wire rope, and detection errors of 2.43%, 3.44%, 2.11%, and 3.39% for wear at 4 positions of the steel wire rope. In response to the problem that the detection precision of the original steel wire rope safety monitoring system for the main shaft hoist of Gubei Coal Mine, Huaihe Energy Holding Group, cannot meet the on-site requirements, the proposed method is adopted to improve the original system. The on-site application results show that the accuracy of wire rope breakage detection is increased from 80% to 96%, the damage positioning error is reduced from 500 mm to within 300 mm. The damage positioning accuracy is increased from 75% to 98%, the real-time detection rate of damage is increased from 76% to 90%, and the tail rope distortion detection rate is increased from 70% to 85%.