LI-YOLO: An Object Detection Algorithm for UAV Aerial Images in Low-Illumination Scenes - Songwen Liu; Hao He; Zhichao Zhang; Yatong Zhou (Drones)
With the development of unmanned aerial vehicle (UAV) technology, deep learning is becoming more and more widely used in object detection in UAV aerial images; however, detecting and identifying small objects in low-illumination scenes is still a major challenge. Aiming at the problem of low brightness, high noise, and obscure details of low-illumination images, an object detection algorithm, LI-YOLO (Low-Illumination You Only Look Once), for UAV aerial images in low-illumination scenes is proposed. Specifically, in the feature extraction section, this paper proposes a feature enhancement block (FEB) to realize global receptive field and context information learning through lightweight operations and embeds it into the C2f module at the end of the backbone network to alleviate the problems of high noise and detail blur caused by low illumination with very few parameter costs. In the feature fusion part, aiming to improve the detection performance for small objects in UAV aerial images, a shallow feature fusion network and a small object detection head are added. In addition, the adaptive spatial feature fusion structure (ASFF) is also introduced, which adaptively fuses information from different levels of feature maps by optimizing the feature fusion strategy so that the network can more accurately identify and locate objects of various scales. The experimental results show that the mAP50 of LI-YOLO reaches 76.6% on the DroneVehicle dataset and 90.8% on the LLVIP dataset. Compared with other current algorithms, LI-YOLO improves the mAP 50 by 3.1% on the DroneVehicle dataset and 6.9% on the LLVIP dataset. Experimental results show that the proposed algorithm can effectively improve object detection performance in low-illumination scenes.