Abstract: To solve the problems of few detection categories and lack of unified comprehensive evaluation index in the existing transmission line detection, this paper proposes a deep learning-based transmission line defect detection and evaluation method. Defects are treated as a category by building a Wire_10 based transmission line dataset. In order to further reduce the target detection of images due to the attachment of nests and foreign objects in the line, which is easily affected by the background and lighting, these two factors are used as variables to define the background dataset and lighting dataset, and a new method based on R-CNN end-to-end high recognition accuracy deep learning algorithm is proposed for building detection models with transfer learning and fine-tuning. The results show that the detection method can accurately identify defect categories in the Wire_10 dataset and is robust to aerial images with complex backgrounds and different illuminations.