Improved YOLOv7-Tiny for Object Detection Based on UAV Aerial Images

Abstract

The core task of target detection is to accurately identify and localize the object of interest from a multitude of interfering factors. This task is particularly difficult in UAV aerial images, where targets are often small and the background can be extremely complex. In response to these challenges, this study introduces an enhanced target detection algorithm for UAV aerial images based on the YOLOv7-tiny network. In order to enhance the convolution module in the backbone of the network, the Receptive Field Coordinate Attention Convolution (RFCAConv) in place of traditional convolution enhances feature extraction within critical image regions. Furthermore, the tiny target detection capability is effectively enhanced by incorporating a tiny object detection layer. Moreover, the newly introduced BSAM attention mechanism dynamically adjusts attention distribution, enabling precise target–background differentiation, particularly in cases of target similarity. Finally, the innovative inner-MPDIoU loss function replaces the CIoU, which enhances the sensitivity of the model to changes in aspect ratio and greatly improves the detection accuracy. Experimental results on the VisDrone2019 dataset reveal that relative to the YOLOv7-tiny model, the improved YOLOv7-tiny model improves precision (P), recall (R), and mean average precision (mAP) by 4.1%, 5.5%, and 6.5%, respectively, thus confirming the algorithm’s superiority over existing mainstream methods.