嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测

Objective: In transmission lines, bolts are widely used as a kind of fasteners to connect various parts of transmission lines and make the overall structure stable and safe. However, bolts are easily damaged because of their complex working environment. The damage or loss of a bolt may cause a large area of transmission line failure, which seriously threatens the safety and stability of the power grid. Bolts are the most common components of transmission lines. Thus, bolt defect detection is an important task in transmission line inspection. Good features are difficult extract because of the complex background, small target, small difference between categories, and loss of gradient information. This study proposes a dual-attention scheme to enhance the visual features of different scales and positions. Method: First, for different scales, the network extracts the feature map of each layer, uses the multi-scale attention model to obtain the corresponding attention map, calculates the difference of the attention map for adjacent layers, and adds it to the loss function as a regularization term to enhance the fine features of the bolt area. The trained network continuously reduces the difference in the attention maps of different layers. The learned attention maps of different scales are introduced into the network as a kind of context information. This procedure can avoid the loss of important information in the process of feature extraction. No additional regulatory information is required because the attention map is from the network itself. Second, for different positions, bolts appear in specific positions of the accessories, but due to light blocking and other reasons, the characteristics of these positions are not obvious. In this study, we use the feature map to derive a spatial attention map of the image. Each element in the attention map indicates the degree of similarity between two spatial locations. Then, the attention map is used to combine the features of each position with the global feature. This process enhances the features in similar regions and improves the difference degree between dissimilar areas. Hence, the difference between the bolt and the background is increased, and the detection accuracy of the bolt area is improved. Result: The method is tested on a typical bolt data set for aerial transmission lines. The typical bolt data set contains 1 483 images of three types of bolts. Each image has a size of approximately 3 000×4 000 pixels. A total of 2 692 targets are labeled, and they include 1 443 normal bolt samples, 670 missing bolt samples, and 579 missing nut bolt samples. The ratio of the training set to the test set is 8 :2. The baseline model used in this study is the faster region convolutional neural network(Faster R-CNN) model. Experimental results show that compared with the baseline, the proposed model's mean average precision (mAP) is increased by 0.29% when the multi-scale attention module is added. Normal, missing and missing nut bolts increase by 0.62%, 2.54%, and 0.69%, respectively. After the addition of the spatial attention module, the mAP of the model increases by 0.61%; specifically, the AP of normal bolts increases by 0.3%, that of missing bolts increases by 2.05%, and that of missing nut bolts increases by 0.52%. This result is obtained because several shaded nuts of missing bolts are confused with the nuts of normal bolts, leading to misjudgment. After introducing multi-scale attention and spatial attention at the same time, the model's mAP is increased by 2.21%; the AP of the normal, missing, and missing nut bolts is increased by 0.29%, 5.23%, and 1.10%, respectively. These experimental results prove the effectiveness of the bolt defect detection method for aerial transmission lines based on the dual attention mechanism. This study also conducts visualization experiments, including the establishment of feature maps, model training loss function curve, precision-recall(PR) curve, and bolt defect detection result map, to prove that the proposed method can be applied to feature extraction. Conclusion: Experimental results prove that the proposed detection method for aerial transmission line bolt defects based on the dual attention mechanism is effective. The process of supervising feature extraction can ensure that abundant useful information is retained when extracting features. For the bolt defect detection task, increasing the difference between the target and the background can improve the detection accuracy of the target area. The visualization experiments verify that the proposed method can retain abundant useful information in the process of feature extraction. The visualized test examples also prove that the proposed method can effectively avoid the problem of misjudgment in bolt defect detection.