Abstract: Bolts play a role in fixing the connection between lines in transmission lines.Once loose or detached,it may cause po-wer transmission failures and cause large-scale power outages.Obviously,regular inspection of bolts in transmission lines is essential to ensure the safety and stability of the entire power system.Most of the existing detection methods are based on deep convolutional neural networks.However,the unobvious features and small size of the bolts pose a challenge to the detection work.Aiming at the above problems,this paper proposes a bolt defect detection method for transmission lines based on region attention mechanism and multi-scale feature fusion.Firstly,a region attention module suitable for object detection is proposed,which is embedded in the residual block of ResNet50 to enhance the network’s feature extraction for bolts.Secondly,based on the feature pyramid networks(FPN),a bottom-up path is extended,and shallow features are fully utilized to improve the detection accuracy of small objects.Finally,in order to alleviate the imbalance between samples,the PrIme Sample Attention(PISA) soft sample sampling strategy is introduced.Experimental results show that the proposed method achieves a mean average precision(mAP) of 74.3% and an average recall(AR) of 86.4% with a detection speed of 8.2 FPS when detecting transmission line bolts.Compared with other detection networks,the proposed method improves the detection accuracy of bolt defects without sacrificing too much detection speed.

Key words: Transmission line bolts, Small object detection, Attention mechanism, Multi-scale feature fusion, Sampling strategy