The small sample size of transmission line foreign body invasion online monitoring imagea, and of the image features of foreign body present a problem. Thus a deep learning method for foreign body invasion monitoring and recognition of transmission lines is proposed. First, the images of typical transmission lines and foreign bodies in normal operation are selected, and the images of transmission lines invaded by foreign bodies are expanded by means of a conditional generation antagonism network algorithm. Then the Dense-net network is replaced by the penultimate network in the YOLOv3 network, and the deep learning network model of Dense-YOLOv3 is established. A real image sample and the extended image sample data are used to train and test the deep learning network to realize the foreign body intrusion monitoring and recognize the transmission line. This deep learning network can effectively identify foreign objects such as kite, model aircraft, garbage and mechanical construction, with a recognition accuracy reaching 98%, 96%, 90% and 100%, respectively. This work is supported by the Science and Technology Project of State Grid Corporation of China (No. SGITG- 2018ZXCG-FF).