Remote tiny object detection based on improved YOLOv7

Unmanned aerial vehicle (UAV) aerial imagery typically presents challenges such as complex backgrounds, small-sized targets, and wide fields of view, making object detection more difficult. So, this paper proposes a novel method that improves upon the YOLOv7 algorithm. Firstly, it introduces the BiFormer attention mechanism to help the model effectively capture the correlation between target objects, enabling better identification and localization of small objects, thus providing more accurate object detection results. Secondly, to address the issue of standard convolution's inability to share parameters, the RFAConv convolution module is embedded in the E-EALN module to extract spatial features of the receptive field, further enhancing the model's performance. Lastly, the boundary box regression loss function is improved to optimize cases where predicted boxes have the same aspect ratio as ground truth boxes but completely different width and height values, a problem existing boundary boxes cannot address. It demonstrate that the YOLOv7 algorithm haves a 1.5% added in mAP0.5. Additionally, it is better suited for tiny target detection tasks.