Defect Detection of Steel Wire Rope in Coal Mine Based on Improved YOLOv5 Deep Learning

Abstract

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipmentof the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedinglycrucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining tothe flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementionedproblems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an objectbased on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defectsoutside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of smallsample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detectionand solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidentsoccasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wirerope defect detection, the average correctness rate and the average accuracy rate of the model are significantlyenhanced with those before the modification, and that the detection speed can be maintained at a real-time level.