For the traditional human non-destructive testing and recognition methods,there are problems of insufficient accuracy and reliability as well as few kinds of defects to detect.To solve them,this paper proposes an aerospace composite material defect detection algorithm incorporating frequency domain features.The algorithm can be divided into three main steps.Firstly,the input information of the frequency domain of the image is added to the feature extraction backbone network which is used to improve the feature extraction effect of defect images.Secondly,a module of informational concentration is proposed in order to improve the visualization capability and detective accuracy of defects,and on the basis of mask region-based convolutional neural network (Mask R-CNN),the segmentation mask loss function is improved.Finally,combined with the cascaded neural network structure of cascade region-based convolutional neural network (Cascade R-CNN),a new instance segmentation network is formed.In addition,the proposed instance segmentation network was experimentally verified in the aerospace composite material defect X-ray image data set,and the average accuracy of the model detection reached 95.3%,which achieved better results than other instance segmentation algorithms,such as Mask R-CNN and cascade mask region-based convolutional neural network (Cascade Mask R-CNN).The research result has been applied to the intelligent detection of several common aerospace composite material defects in actual industrial production.