Aiming at solving the problems of low accuracy and slow speed in the surface defects detection of aeroengine components using traditional methods, an improved YOLOv4 algorithm is proposed herein. First, an aeroengine component surface defects dataset was developed and the K-means clustering algorithm was suggested to cluster the defect samples for obtaining the priori anchor’s parameters of different sizes. Second, the improved parameter-adjustment algorithm was used to scale the priori anchor’s sizes and increase the difference in sizes to improve the matching rate between priori anchors and feature maps. Finally, convolution layers were added after the different feature layers of the backbone feature extraction network output and spatial pyramid pooling structure to improve the ability of network to extract defect features. Experimental results show that the mean average precision (mAP) value of the improved YOLOv4 algorithm in the test dataset is as high as 82.67%, which is 4.55 percent point greater than that of the original YOLOv4 algorithm. The average detection time of a single image is 0.1240 s, which is basically the same as that of the original algorithm. Moreover, the detection performance is better than Faster R-CNN and YOLOv3.