Aiming at the massive X-DR images generated by crimping quality detection of transmission line strain clamps, an intelligent 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 carried 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 transmission line operation and maintenance personnel to carry out automatic recognition and defect detection of strain clamp crimping images.