In the field experiment,due to the lack of obvious defect characteristics in infrared detection of civil aircraft composite skin defects,resulting in low detection accuracy and complex model leads to the slow detection speed.To solve these problems,an improved SSD algorithm is proposed to enhance the detection precision and realize the model lightweight.Firstly,U Net network is used for image preprocessing to reduce the interference of irrelevant feature information and enhance the detectability of defects.Secondly,Mobilenetv2 is used as the backbone network to reduce the memory size of the model and improve the efficiency of defect detection.Then,the inverse residual module of Convolutional Block Attention Module(CBAM) serves as an auxiliary convolution layer to further lightweight the model and address precision reduction.The ablation experiments and comparison experiments show that the mAP accuracy of the proposed algorithm is as high as 96.8% on the defect data set of civil aircraft composites,with a detection speed of 72.74 f/s(FPS).Compared with the traditional SSD algorithm,AP0.5 improves by 8.3%,the number of parameters counts(Params) is reduced to 3.966 M,and the number of floating points(GFLOPS) is reduced by 42 times.This algorithm has a good application prospect in the field of infrared detection of aircraft composites.