Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output

In view of the problems such as missing detection and false detection of complex scene aircraft in remote sensing image by the YOLOv3 algorithm, an aircraft detection algorithm based on adaptive feature fusion and multiscale output is proposed. Firstly, K-means ++ is used to cluster the data set instead of K-means algorithm, which solves the instability of the initial clustering center of k-means. Then, on the basis of YOLOv3 network, a scale with resolution information is added, which is more conducive to the detection of small target aircraft. Finally, an adaptive feature fusion layer is added before the four scale output of the network model, which solves the problem of insufficient feature fusion at different scales and reduces or eliminates the influence of negative samples on reverse conduction. The experimental results show that the detection accuracy of the improved algorithm reaches 96.17% in the remote sensing image, which is 2.6% higher than that of the algorithm.