Detection Model for Small Objects in UAV Image Scene

Due to the flying altitude and shooting angle of UAV, many small targets can be seen in the captured images. The small objects have so few pixels and semantic information that can easily be disturbed by complex background information. Meanwhile, aggregation often takes place. Accurate detection of small targets is the key to improve the performance of the detection model for UAV. In this paper, a detection model called UAIDet for small objects is proposed. An adaptive channel fusion module is developed. In the feature fusion stage, the channel weights are dynamically learned to filter out the information conflicts between the different features levels, to improve the detection ability for small targets. In addition, an offset-sensitive loss function is developed for the location. In the convergence phase of the small object bounding box, the offset-sensitive term solves the sensitivity for geometric error by using root function. The model UAIDet is tested in dataset Visdrone2022, the mAP and AP50 reach 22.0% and 37.1% respectively, which is 3 and 4.7 percentage points higher than that of the benchmark model. The experiment in TinyPerson dataset shows 9.9% mAP and 29.1% AP50, improve 4.29 and 4.2 percentage points separately. The results have verified the robustness and effectiveness of UAIDet model. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.