KAS-YOLOv5: A Novel Target Detection Algorithm for Aerial Images

Target detection in aerial image is a challenging task due to the high proportion of small targets, severe occlusion, and large scale variations. In this paper, a novel KAS-YOLOv5 algorithm is proposed based on YOLOv5. First, the K-Means++ clustering algorithm is used to regenerate the initial anchor boxes suitable for the target size of the aerial image. Secondly, Adaptively Spatial Feature Fusion (ASFF) is used to solve the inconsistency problem between features of different scales in aerial images. Finally, a dynamic label assignment strategy Simplified Optimal Transport Assignment (SimOTA) is used to transform the label assignment into an optimal transmission problem, thereby improving the severe occlusion phenomenon. The experimental results demonstrate a high accuracy rate of 42.2% on the VisDrone2019 dataset, outperforming other algorithms.