With the continuous development of modern power systems and the increasing scale of power grids, faults caused by external damage have become one of the main causes of overhead transmission line failures. In overhead transmission line video surveillance, false or missed alarms occur when using the traditional bounding box target detection method for external damage warning. The high cost of integrating the pixel-level mask annotation data used in the training of the mask instance segmentation neural network (Mask-RCNN) limits the large-scale application of this algorithm. To address these problems, this paper applies an improved Mask-RCNN network to the field of transmission line external breakage target detection by using bounding box annotation instead of partial mask annotation in the process of dataset annotation. During training, the features of the detection branch are migrated to the mask branch. The experimental results show that the improved algorithm can achieve an average recognition accuracy of more than 91% for common external damage categories with 80% of the mask labeled samples, providing a feasible idea for accurate identification and segmentation of external damage hazards on transmission lines. This work is supported by the National Natural Science Foundation of China (No. 61502297).