Aiming at the problem that the quality of the captured image may be poor in the inspection of transmission lines, and the problem that the detection accuracy of traditional methods is not high due to the line defect that the targets are small and densely distributed, a transmission line defect detection method based on super-resolution reconstruction and multi-scale feature fusion is proposed. First, the super-resolution network is used to reconstruct the inspection image, improve the clarity and enrich the feature information contained in the image. Then the improved YOLOX network is used to detect defects in the inspection image, and the convolution block attention mechanism is embedded in the backbone network to strengthen the positioning ability of the model for overlapping small targets. In order to further improve the detection ability of small targets, a shallow detection scale is added to YOLOX’s feature fusion network for feature fusion. Finally, by using CIOU to optimize the loss function of the bounding box, improve the convergence ability of the model and reduce the missed detection rate of the defect targets. The experimental results show that the proposed method can accurately detect the transmission line defects on the basis of improving the inspection image quality, with an accuracy of 93. 27%. Compared with classical models such as SSD, it has stronger extraction ability and robustness for small and dense defect targets.