To realize automatic detection of bullet appearance defects and to overcome the limitations associated with traditional machine vision methods, i.e., excessive time required to manually design a target feature and generalization ability is poor in defect detection, we use the K-means++ algorithm to improve the anchor frame generation method and propose a bullet appearance defect detection model based on the improved faster region-convolutional neural network (R-CNN). The proposed model uses a CNN that can automatically extract target features and has strong generalization ability. The detection model is combined with ZFNet, VGG_CNN_M_1024, and VGG16, respectively. Results demonstrate that the detection accuracy of the detection model combined with VGG16 is higher than the others. The results show that that of the improved model demonstrates 97.75% accuracy and the speed reaches 28 frame·s -1.