Detection of Crimping Defects in Strain Clamp X-ray Images Based on FG-YOLOv7-tiny Algorithm
To ensure the safe and reliable operation of transmission lines,the detection of crimp defects in strain clamps had become an important task of power inspection.To this end,a faster neural networks ghost convolution-you only look once version 7-tiny(FG-YOLOv7-tiny)algorithm was proposed for strain clamp crimp defect detection.First,a strain-clamp X-ray image dataset containing five common crimp defects was constructed.Secondly,faster neural networks(FasterNet)were used to replace the efficient layer aggregation networks(ELAN)of YOLOv7-tiny to reduce the model size.Finally,the ghost spatial pyramid pooling cross stage partial connection networks(GhostSPPCSPC)was used to replace the spatial pyramid pooling cross stage partial connection networks used by YOLOv7-tiny to improve the detection accuracy.The experimental results showed that the accuracy and average accuracy of the FG-YOLOv7-tiny algorithm reached 91.30%and 94.28%,respectively,which were 3.99%and 1.59%higher than the original YOLOv7-tiny algorithm.The model size was 22.25 MB,and the detection speed reached 172.41 frames/s,which satisfied the requirements of real-time detection.Therefore,the FG-YOLOv7-tiny algorithm improved the detection accuracy,which could effectively detect the crimping defects of strain clamps and meet the requirements of edge device deployment.
万方数据
维普
