The surface defect detection of train bearing image was carried out by using deep learning Faster R-CNN.The artificial database BSD was established, the defect of insufficient data was made up for through image augmentation.The Faster R-CNN algorithm was adopted for target detection and recognition, and in the convolutional neural network ZF Net model was adopted to train the BSD data set and the detection results were obtained. Finally, the results were compared with those of the traditional Canny algorithm.Experimental results show that the deep learning Faster R-CNN algorithm is superior to the traditional Canny algorithm; for Faster R-CNN algorithm, the bearing defect detection accuracyis 93.03%, the detection time is 0.29 s; compared with the traditional Canny algorithm, the detection accuracy increases 21.73% and the detection time is reduces by 2.21 s, at the same time the precision rate is greatly improved. The Faster R-CNN algorithm can be used to realize accurate detection and identification of bearing surface defects and meet the requirements of railway departments to bearing repair.