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Bolt defect inspection is an important work in transmission line inspection. Due to the small size of bolts in the transmission line inspection images, existing algorithms are difficult to extract valuable features and achieve poor performance on bolt defect detection. This paper proposed an ultra-small bolt defect detection model(UBDDM) based on a deep convolutional neural network(DCNN), including an ultra-small object perception module(UOPM) and a local bolt detection module(LBDM). In this paper, UOPM is first constructed to realize coarse region recognition for the salient region of bolts in the inspection images, and the high-resolution image blocks are obtained from the original image according to the recognition results. Then, LBDM is constructed to intelligently identify the bolt defects from the high-resolution image blocks. Considering that the features of ultra-small targets are difficult to extract, feature extraction networks are constructed based on ResNet-50, and the hybrid attention mechanism and multi-scale feature fusion are introduced to further improve the network’s ability to extract shallow features. This method uses two-stage detection to realize end-to-end bolt defect detection but only needs to provide a single-stage target detection label, which greatly reduces the workload of data labeling. Experimental results show that the proposed method achieves excellent performance on bolt defect detection in inspection images.
Luo et al. (Sun,) studied this question.