Eagle-eye vision system, object detection, unpiloted aerial vehicle (UAV) imagery, YOLOv8, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Object detection in unpiloted aerial vehicle (UAV) imagery has been widely applied across various domains. However, the typically small size and uneven spatial distribution of objects in UAV imagery pose significant challenges for UAV object detection tasks. Confronting such challenges, we propose Eagle-YOLOv8, an object detection algorithm for UAV imagery inspired by the eagle-eye vision system. First, inspired by the double fovea mechanism of the eagle eye, we construct a long-focus attention module, which can promote the network to focus on the target and pay more attention to discriminative features. Second, we propose a feature weight fusion network inspired by the double field of view characteristics of eagle eye. This network utilizes a novel weight fusion technique to alternative the conventional concatenate method, which assigns weights to feature layers according to their importance. Finally, we analyze the effect of wise-IoU loss on the fit of the prediction box to the object. In addition, we create a dataset called AerialDet with eight categories to validate the generalization performance of the proposed method. Experimental evaluations conducted on both the challenging VisDrone2019-Det dataset and our self-collected dataset validate the effectiveness of Eagle-YOLOv8. The proposed method outperforms baseline approaches in object detection performance, exhibiting notable improvements: 8.56% in precision metrics, 10.06% in mAP50 metrics, and 9.43% in recall metrics, all achieved with only a marginal increase in parameters compared to YOLOv8 (small).