Research on Improved Crowd Detection Based on YOLOv5

With the acceleration of the process of modern urbanization and the improvement of residents' material living standards, the flow of people in the public space is gradually becoming saturated. The monitoring equipment in public places records a huge amount of people flow information all the time, but due to the crowds tend to be dense and crowded. Traditional machine learning cannot make accurate and efficient identification of a large number of dense crowds, if the deep learning technology can be used to process the crowded crowd captured by the surveillance camera and accurately identify the number of people in public places, it provides an important guarantee for the flow of people in public areas and safety construction. However, for crowded targets with occlusions, the traditional target detection algorithm sometimes performs poorly. Based on the above background, this paper introduces an enhanced deep learning framework utilizing the YOLOv5 neural network for crowd detection research. aiming at the characteristics of dense and crowded crowds in public areas. By improving convolutional layer C3 in the backbone structure of YOLOv5 neural network and adding CBAM attention mechanism. Compared with the original YOLOv5, the improved model has increased the maximum F1 value of crowd recognition at near, middle and far distances. To sum up, the deep learning framework improved by YOLOv5 neural network proposed in this paper has significantly improved the recognition accuracy of crowded people in public areas.