Abstract: Detecting defects in transmission lines and repairing them in a timely manner is of great practical significance for ensuring the safety and stability of the power grid.However,due to the complex background and small component size of transmission line images,existing object detection models cannot achieve satisfactory results.Therefore,this paper proposes a deep neural network model for detecting defects in transmission lines based on dual-branch serial attention.The model designs dual-branch serial attention(DBSA) to allow the model to focus more weight on the defects,and proposes well-connected feature pyramid network(WCFPN) to enable full fusion of the features extracted by DBSA,thereby improving the model's ability to detect small targets.DBSA compresses the feature map along the height and width branches and extracts attention using one-dimensional convolution to achieve fine-grained control over the features.WCFPN designs a new fusion path that includes cross-scale fusion and skip-layer connections,allowing high-level semantic information and low-level spatial information extracted by DBSA to interact more fully.Finally,experiments are conducted on five transmission line datasets,including insulator explosion,damaged anti-vibration hammer,bird's nest debris,broken cementpole and transmission line defect,and the proposed model achieves the best detection performance.The average AP₅₀and AP of the five datasets is 84.3% and 46.1%,respectively,which is 3.7% and 3% higher than that of the state-of-the-art model YOLOv7.

Key words: Power defect, Object detection, Attention mechanism, Feature pyramid network