Pedestrian detection is a vital study field within object detection. With the development of deep learning, models perform exceptionally well in pedestrian detection for sparse scenarios. However, their performance often fails in complex and crowded dense pedestrian scenarios, particularly when there is a concentration of small objects. Challenges such as low detection accuracy, high miss rates, and high false-positive rates persist in these scenarios. To enhance dense pedestrian detection performance, we propose a model named RT-DETR-LKG, based on RT-DETR. The improved model incorporates LSKA...
