## 5 Conclusions  
This paper proposed a machine-vision method for identifying surface defects on arc magnets. The proposed method combines transfer learning, network pruning, and knowledge distillation for the YOLOv5s model to obtain high recognition accuracy of surface defects while greatly compressing the model size at a slight loss of accuracy, thereby improving the recognition speed. In our work, the type and quantity of the original public image-based dataset of surface defects on arc magnets were appended by us to make the dataset more extensive and representative. To overcome the dependence of model training on massive image-based data of arc magnets, our model was derived from YOLOv5s that had been robustly pre-trained by another publicly available dataset with a large number of different targets other than arc magnets. The transfer-learning mechanism under the frozen and fine-tuned YOLOv5s enabled the target recognition ability obtained by pre-training on the other dataset to be converted into highly accurate surface-defect identification after training based on relatively few image-based arc magnets. The proposed pruning rate, validated by the objective function we designed, achieved an optimal balance between maximizing model compression and minimizing accuracy loss during the network pruning for the transferred YOLOv5s. The unpruned and pruned transferred YOLOv5s were respectively employed as the teacher and student networks for knowledge distillation. A proposed \(\lambda\) weighting factor was introduced into the confidence loss function of knowledge distillation to increase the sensitivity of the student network for extracting and identifying image-based features of surface defects, but such a sensitivity improvement was bound to sacrifice a small amount of recognition accuracy for defect-free arc magnets. The experimental results show that our model is only 1.921 MB in size and can identify any defective arc magnet with 100% accuracy within an average inference time of 9.46 ms. Moreover, the misidentification rate for defect-free arc magnets did not exceed 1%. Considering accuracy, speed, and size together, our model outperforms other conventional lightweight models and is more conducive to high-precision and rapid identification of surface defects on arc magnets under a lightweight model deployment with low computing  
Figure 10: An example of the detection of defective insulators using our model: (**a**) the schematic of the insulator defect detection and its images; (**b**–**e**) the identification effect of different defective insulators synthesized with ground-truth images. power. Similar superior performance was also obtained for the detection of insulator defects in a high-voltage tower, for which our model was used to identify image-based insulator data. Given the advantages of our model in compressing models and improving accuracy, more applications based on our method have the potential to be developed. Even though the identification accuracy of defective arc magnets reached 100%, 1% of defect-free arc magnets still could not be accurately identified. The significant compression of the model size did not result in a substantial reduction of inference time. These phenomena make it necessary to continue improving our method in terms of accuracy and speed, but the corresponding improvements are also limited by the performance of YOLOv5s. Along with developing the YOLO series, we will attempt newer and better YOLO models to update YOLOv5s for the surface-defect identification of arc magnets in future work. Meanwhile, we will continue to explore different model compression methods to achieve faster identification speed. Conceptualization, Q.H. and Y.Z. ; methodology, Q.H. ; software, Y.Z. ; validation, Q.H., Y.Z. and T.Y. ; formal analysis, Q.H. and K.Y. ; investigation, Q.H. ; resources, Q.H. ; data curation, Q.H. ; writing--original draft preparation, Q.H. and Y.Z. ; writing--review and editing, Q.H. ; visualization, Q.H., T.Y. and K.Y. ; supervision, Q.H. ; project administration, L.C.