一种融合RepVGG和YOLOv5的行人检测方法

现如今，基于YOLOv5的网络模型被广泛应用在行人检测的任务中，在精度和速度上有着良好的效果。但在终端设备上部署使用，往往受到算力的限制。因而，基于RepVGG模型改进的主干网络，并且为了提高在密集人群和复杂环境下的适应性，加入了坐标注意力机制，扩大感受野的同时增强感兴趣区域的权重。经过实验测试，这种轻量化的网络参数量和计算量比较小，而且检测精度和鲁棒性也比较高，能够在一定程度下满足工程应用的要求。

A pedestrian detection method of integrated RepVGG and YOLOv5

Nowadays, the detection model based on YOLOv5 network are widely used in the task of pedestrian detection, and have good results in terms of accuracy and speed. However, it is often limited by computing power on terminal devices. Therefore, this paper is based on the improved backbone network of the RepVGG model. In addition, in order to improve the adaptability in dense crowds and complex environments, a coordinate attention mechanism is introduced to expand the receptive field and enhance the weight of the region of interest. After experimental testing, this lightweight network parameter and calculation amount is relatively small, and the detection accuracy and robustness are relatively high, which can meet the requirements of engineering applications to a certain extent.