【摘要】 为解决目前对高密度人群计数问题,提出了一种基于Yolov5的人群计数方法。其中输入层主要进行Mosaic数据增强,即自适应锚框和自适应的图片缩放技术;Backbone中Yolov5主要采用Focus和CSP(Cross Stage Partial)结构;Neck层采用SPP(Spatial Pyramid Pooling)模块和FPN(Feature Pyramid Networks)+PAN(Pixel Aggregation Network)结构;输出端主要针对Bounding Box损失函数采用了CIOU_Loss作为损失函数和DIOU_loss作为NMS(Non Maximum Suppression)的平均指标;最终输出训练结果。实验结果表明,该方法能有效提高人群计数的精度。更多还原

【Abstract】 In view of the current limitations of counting high-density population, a population counting method based on Yolov5 is proposed. The input layer is mainly used for Mosaic data enhancement, adaptive anchor frame and adaptive picture zooming technology. Backbone Yolov5 mainly adopts Focus and CSP(Cross Stage Partial)structure. The Neck layer mainly adopts SPP(Spatial Pyramid Pooling) module and FPN(Feature Pyramid Networks)+PAN(Pixel Aggregation Network) structure. The output end adopts CIOU＿Loss as the loss function mainly for Bounding Box Loss function and DIOU＿Loss as average indicator of the NMS(Non Maximum Suppression). Finally the training results are output. Experimental results show that this method can effectively improve the accuracy of crowd counting.更多还原