An improved YOLOv7-tiny algorithm for dense pedestrian detection

In view of the missing detection and false detection of the current pedestrian detection model in the dense pedestrian scene, a dense pedestrian detection algorithm based on Yolov7-tiny was proposed. In order to solve the problem of missing pedestrian target features in dense pedestrian detection scenarios, the dynamic convolution module and the improved ROELAN module are used in the backbone network to make the network focus on the important features of dense pedestrians through dynamic convolution, which effectively reduces the impact of feature loss on model detection. In order to solve the problem that small target pedestrians are easy to be missed in dense pedestrian detection scenarios, the BIFPN idea, CARAFE upsampling operator and the improved Rep-ELAN-B module are introduced to improve the detection performance of the model by using the small target feature information in the medium and low-dimensional feature maps. The average detection accuracy of the improved model reached 83.4%, which was 2.7% higher than that of the baseline model. The experimental results show that the proposed algorithm can be better applied to dense pedestrian detection scenarios.