Small Target Detection Algorithm Based on Wavelet Domain Features and Weighted Sample Allocation Strategy

Abstract

Small target detection using UAV aerial photography has emerged as a popular research topic. The resolution of small target images is low and the background is complex. In this paper, utilizing a single-stage target detection algorithm ATSS as its foundation, we propose a small target network detection model SDW-Net that combines deformation convolution with wavelet domain feature enhancement by weighting the samples from both classification and regression tasks, and combining the strong local feature representation ability of discrete wavelet transform. Firstly, the original features are obtained by using discrete wavelet transform, and the weighted wavelet features are obtained through the deformable convolution block and the spatial channel attention unit; secondly, the weighted features of the regular domain are obtained through the inverse wavelet trans-form; finally, the original feature and the wavelet features are fused to enrich the details of the small target image and sent to the detection head for output. According to the experimental findings, when compared to the ATSS algorithm, the proposed detection model achieved an average increase in prediction accuracy of 7.2%, and a recall rate increase of 3.7%, and the problem of missing detection and false detection can be alleviated, which can provide a new technical solution for small-sized target detection scenarios.