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With the development of economy and society, power generation and electricity consumption increase year by year. Safe power supply is related to national economy and people"s livelihood. In the process of many years of use, due to the transmission of power transmission line is often influenced by the external environment, making it easier for the transmission line components appear different degree of damage. The pin is the key to the fixed nut parts. The shedding of pin will lead to an unstable connection between the components that brings great challenge to the safe operation of power transmission network. With the application of deep learning technology in the field of computer vision, the machine automatic identification pin which is a tiny part in the transmission line system has become a reality. In this paper, Faster R-CNN algorithm was used to identify pin shedding fault in Unmanned Aerial Vehicle (UAV) patrol image, and the impact of different classifiers on recognition results was discussed. Then Aggregate Channel Features (ACF)+Adaboost, Hough+ Line Segment Detector (LSD) and Faster R-CNN recognition methods were compared. The experimental results show that the recognition rate of Faster R-CNN based target detection method for pin falling fault in transmission lines can reach 96%, and the recognition rate of normal pin can reach 98% at the same time.