Research on a small target object detection method for aerial photography based on improved YOLOv7

In aerial imagery analysis, detecting small targets is highly challenging due to their minimal pixel representation and complex backgrounds. To address this issue, this manuscript proposes a novel method for detecting small aerial targets. Firstly, the K-means + + algorithm is utilized to generate anchor boxes suitable for small targets. Secondly, the YOLOv7-BFAW model is proposed. This method incorporates a series of improvements to YOLOv7, including the integration of a BBF residual structure based on BiFormer and BottleNeck fusion into the backbone network, the design of an MPsim module based on simAM attention for the head network, and the development of a novel loss function, inner-WIoU v2, as the localization loss function, based on WIoU v2. Experiments demonstrate that YOLOv7-BFAW achieves a 4.2% mAP@.5 improvement on the DOTA v1.0 dataset and a 1.7% mAP@.5 improvement on the VisDrone2019 dataset, showcasing excellent generalization capabilities. Furthermore, it is shown that YOLOv7-BFAW exhibits superior detection performance compared to state-of-the-art algorithms.