Aiming at the problems of low detection accuracy and inefficient detection in defect detection of aeroengine parts,an algorithm based on improved Faster R-CNN was proposed.In this algorithm,deep residual network was used to extract features of defects.The pyramid model with content-aware reassembly of features(CARAFE) was used to integrate the feature maps of each level,and the corresponding level feature maps were selected for detection and recognition according to the scale of anchor boxes.In the part of RCNN,stratified sampling was used to mine hard samples to enhance its learning.The experimental results show that the proposed algorithm possesses higher detection accuracy and the detection speed is improved effectively.