Research on improved YOLOv7 algorithm for small target detection in aerial photography
A YOLOv7 based improved algorithm was proposed to address the issues of low detection accuracy and high missed detection rate in small object detection due to small target pixels,dense distribution,and mutual occlusion.Firstly,replace the SiLU activation function with the ACON activation function to enhance the algorithm's fitting ability;replace the SPPCSPC structure in the algorithm with SPPFCSPC,and improve SPPFCSPC by combining BiFormer attention mechanism to improve model detection performance and accelerate model speed;using SPD-Conv convolution to improve the MP structure in the algorithm and enhance the feature extraction ability of the model;introduced auxiliary boxes in WIoU to design Inner-WIoU,and improved YOLOv7's loss function by combining NWD loss function.Through experiments on the VisDrone2019 dataset,it had been proven that the improved algorithm outperforms the original algorithm without adding too many parameters mAP@0.5 improved by 4.39%,verifying the effectiveness of the improved algorithm in improving model performance.
small target detectionactivation functionattention mechanismfeature extractionloss function
NETL
NSTL
万方数据
