To solve the problem of slow response and low accuracy in the traditional image recognition method of transmission line target inspection,an improved faster-region convolutional neural network (Faster-RCNN) deep learning recognition algorithm is proposed. In this paper,the image features are extracted by zeiler and fergus net (ZFnet) and the ZFnet model parameters are reset to obtain more target details. Then,the Faster-RCNN is used to detect the target. The target candidate box is generated by the sub-network region proposal model and the parameters are tuned by the fast-region convolutional neural network (Fast-RCNN). In addition,the refining stage is introduced into the output part of the Faster-RCNN to increase the refinement of classification and regression of the target features. And then the multiple bounding boxes with the target are combined to achieve accurate classification and coordinate positioning. The results of the experiments show that the improved Faster-RCNN algorithm can effectively identify the transmission line equipment and its defects. The overall recognition rate of the method could reach 93.5% within 1 s of the response time. Compared with the image recognition and the deep learning such as single shot multibox detector (SSD) and you only look once (YOLO),the proposed algorithm improves the identification accuracy and response speed of power equipment,and has certain advantages in intelligent inspection of transmission lines.