When using ferrography to evaluate the wear state of gear box,it is difficult to extract the characteristics of wear particles in gearbox oil tank,and the number of wear particles identification is limited.In view of the above problems,based on the image characteristics of ferrography,the identification of abrasive particles with ferrography based on the Faster R-CNN algorithm was proposed.The algorithm adopts ResNet-34 network to complete the automatic extraction of ferrography abrasive grain features,and adopts RPN network to realize the recognition of multiple abrasive grains in the image.The Batch_Size and learning rate hyperparameters were optimized through experiments,and the experiments were carried out by using the Faster R-CNN algorithm after hyperparameter optimization.The results show that this method overcomes the difficulties of identification caused by abrasive grain intersection,which can identify multiple grinding particles in the image,and count the number of all kinds of grinding grain with high accuracy.Even in the case of different background color of the abrasive grain and noise interference,this method still can accurately judge the type of abrasive grains and has good stability.