Crimping Defect Detection of Transmission Line Strain Clamp Based on X-DR Image and YOLO-MS Model
Aiming at the massive X-DR images generated by crimping quality detection of transmission line strain clamps,an intel-ligent recognition method for crimping defects is proposed based on YOLO-MS model.A X-DR image dataset including 6 typical crimping defects is constructed using the field crimping quality detection images of strain clamps,and the image preprocessing is car-ried out by Gaussian filtering,histogram equalization,and gamma correction.The multi-scale(MS)object detection network YOLO-MS is constructed using CSPDarknet,CBAM-PANet,and Head-4.The model is trained and tested using concentrated training and testing samples from a dataset.The results show that the YOLO-MS model can effectively detect 6 types of strain clamp crimping defects,with a mean average precision of 92.57%,and a detection speed of 26 frames per second.It can be used to assist transmis-sion line operation and maintenance personnel to carry out automatic recognition and defect detection of strain clamp crimping images.
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