Application of Improved YOLOv7 Algorithm in Pedestrian Detection

Aiming at the problems such as low accuracy of pedestrian detection and poor effect of small target pedestrian detection by traditional detection model, an improved YOLOv7 pedestrian detection algorithm is proposed. First of all, in order to improve the positioning accuracy and reduce the target missing detection rate, the NWD module for small target pedestrian detection is used to improve the IOU. Mean-while, the improved CBAM attention mechanism is introduced in this paper, and the optimized C3-Faster module is introduced into the Backbone of YOLOv7 to improve the model accuracy. Combined with the improved CBAM attention mechanism, a new lightweight and high-precision CB$A$MC3Fast module is proposed. To test model performance, a new data set is built based on CUHK Occlusion Datase data set. Several comparison experiments are designed to verify the effectiveness of the algorithm. The experimental results on the self-established data set showed that compared with the previous model, the improved model achieved 2.20% improvement in Precision, 1.97% improvement in Recall, and 1.34^% reduction in box_loss, thus reducing the false detection rate. The weight size of the pro-posed algorithm is only 40.8MB, which has a smaller model volume and higher detection accuracy, so that the algorithm can be adapted to more scenarios, and can play an important role in the fields of traffic management, epidemic prevention and temperature measurement and video surveillance.