Abstract: It is an important task to ensure the normal operation of transmission lines to realize the automatic inspection of fittings and their defects accurately. To alleviate the problem that various existing metal fittings and their defect detection methods lack context information, which leads to false detection and re-detection, this paper proposes a transmission line metal fittings and their defects detection method based on context structure reasoning, which adds structure knowledge after the detection model outputs the detection results to improve the accuracy of the model. First, the image is input into the object detection model; Then, the output results of the detection model are sent to the structural reasoning module, namely, the detection results are mapped to the sequence X to form a vector, including the detection box category, box coordinates, and detection confidence, and then the vector is sent to GRU and self-attention for processing. Using the structural knowledge of the transmission line fittings and their defects, the confidence in the true positive samples is increased, and the confidence of the false positive samples is reduced. Finally, the final output result is obtained through the regressor to achieve the purpose of improving the average precision. The experimental results show that, after adding the structural reasoning module, the \bar P 's value of the baseline model is improved, and the \bar P_50 is up to 6% higher than that of the baseline model, which provides a new idea for improving the detection accuracy of transmission line fittings and their defects.