Environmentally adaptive fast object detection in UAV images

Abstract

Detecting objects in aerial images poses a challenging task due to the presence of numerous small objects and complex environmental information. To address these problems, we propose an efficient detector specifically designed for aerial images, named EAF-YOLOv8, based on YOLOv8-S. In this paper, we introduce a novel backbone network called EAFNet, specifically designed for small object detection. EAFNet consists of the Rapidly Merging Receptive Fields Aggregation Module (RMRFAM) and Multi-Scale Channel Attention (MSCA). The RMRFAM utilizes dilated convolution (DConv) and partial convolution (PConv) to acquire richer receptive fields, capturing more extensive contextual information at higher levels while reducing redundancy in channel information, thereby accelerating inference speed. Furthermore, inspired by denoising tasks, we focus on the feature information surrounding the target background and propose MSCA. MSCA integrates channel attention with an embedded self-attention feature pyramid, extending the feature learning scope to the surrounding environment of the target, beyond the target itself. This approach utilizes enhanced background features to elicit a higher response for small targets, reducing false positives. Experimental results demonstrate that in UAVDT and VisDrone2019, the proposed EAF-YOLOv8 achieves mAP50 scores of 34.3% and 49.7%, respectively. Additionally, EAF-YOLOv8 exhibits high real-time inference speeds of 77.60 FPS and 55.56 FPS, showcasing competitive detection performance.