FedsNet: the real-time network for pedestrian detection based on RT-DETR

In response to the problems of complex model networks, low detection accuracy, and the detection of small targets prone to false detections and omissions in pedestrian detection, this paper proposes FedsNet, a pedestrian detection network based on RT-DETR. By constructing a new lightweight backbone network, ResFastNet, the number of parameters and computation of the model are reduced to accelerate the detection speed of pedestrian detection. Integrating the Efficient Multi-scale Attention(EMA) mechanism with the backbone network creates a new ResBlock module for improved detection of small targets. The more effective DySample has been adopted as the upsampling operator to improve the accuracy and robustness of pedestrian detection. SIoU is used as the loss function to improve the accuracy of pedestrian recognition and speed up model convergence. Experimental evaluations conducted on a self-built pedestrian detection dataset demonstrate that the average accuracy value of the FedsNet model is 91 \(\%\) $\%$ , which is a 1.7 \(\%\) $\%$ improvement over the RT-DETR model. The parameters and model volume are reduced by 15.1 \(\%\) $\%$ and 14.5 \(\%\) $\%$ , respectively. When tested on the public dataset WiderPerson, FedsNet achieved the average accuracy value of 71.3 \(\%\) $\%$ , an improvement of 1.1 \(\%\) $\%$ over the original model. In addition, the detection speed of the FedsNet network reaches 109.5 FPS and 100.3 FPS, respectively, meeting the real-time requirements of pedestrian detection.