Objective Regular inspection and maintenance of insulator defects play a crucial role in ensuring the safety of transmission lines. In order to address issues such as low detection accuracy and poor universality of existing insulator defect detection methods an algorithm based on an improved Detection Transformer DETR was proposed. Methods An improved encoder was designed to use four Transformer stages to capture the feature information of different scales and relationships in the image. At the same time the intermediate output features of ResNet50 were also used to supplement the output features of the layered Transformer thereby enhancing the performance of the object detection algorithm. The improved decoder was designed and a three-layer series structure was adopted to ensure that the decoder can receive and learn feature maps of different scales at different stages. Moreover the feature fusion enhancement module and the query update module can make the decoder learn image feature information more effectively and reduce the difficulty of matching regions with similar semantic features further improving the accuracy of network detection. Results Simulation experiments were conducted on aerial images of insulator defects in transmission lines. The improved method achieved recognition accuracies of 99. 5% and 80. 4% at different thresholds respectively which were 3. 4% and 6. 1% higher than those of the original algorithm. It exhibited good detection performance for partially occluded targets and demonstrated superior detection accuracy and generalization ability compared with other algorithms. Conclusion The improved DETR demonstrates higher detection performance enabling accurate detection of insulator defects. This provides assurance for the detection of other targets on transmission lines in the future such as vibration dampers and spacer rods.