关键词: 无人机（UAV）, 目标检测, 图像增强, YOLOv8, 低照度, 自适应, 频域分离

Abstract: Under natural lighting conditions, UAV (unmanned aerial vehicle) aerial images contain low-light images, which affect the accuracy of target detection, and detection tasks based on aerial images often have high real-time requirements. To address the above problems, a feature-level adaptive enhancement target detection algorithm for UAV aerial images is proposed. Firstly, an improved Laplace operator is fused with the IAT (illumination adaptive transformer) image enhancement network to enhance the edge features of the target and improve the target detection ability. Secondly, a two-branch structure is used to learn the normal image and the enhanced image in parallel, and the learnt features are fused in an adaptive selection manner to construct a BLENet (bad lighting enhancement net) feature-level adaptive enhancement network, which can adapt to the illumination and enhance the information of the image automatically. Then, the deformable convolutional FS-DC module based on frequency domain separation and the FS-C2F module with parameter sharing are designed so as to reduce the number of model parameters and computational redundancy while enhancing the ability of capturing high-frequency information. Finally, the regression loss function Wise-IoU is improved so that the model further focuses on the medium- and high-quality anchor frames, thus reducing the boundary regression loss and improving the localization accuracy. Experimental results on the publicly available dataset visDrone2023 show that compared with the baseline model, the final model improves the mAP@0.50 by 2.3 percentage points while keeping the FPS at 99 frames per second, which makes it suitable for UAV aerial photography real-time inspection and monitoring tasks under the environment of varying light in the open air.

Key words: unmanned aerial vehicle (UAV), object detection, image enhancement, YOLOv8, low illumination, adaptive, frequency domain separation