摘要
针对传统夜间行人识别过程中存在的方法识别速度慢、精度低、识别效果差的问题,提出了一种改进的YOLOv5s夜间行人识别算法。首先采用C3CSGC模块替换YOLOv5s原网络模型中的C3模块。其次,将YOLOv5s的损失函数CIoU换为EIoU。最后,将YOLOv5s模型的特征金字塔换成加权双向特征金字塔BiFPN。实验结果表明,对于夜间行人识别算法的改进,针对原始的YOLOv5s模型准确率,召回率分别提升了4.1%和5.9%,mAP_0.5值提升了7.2%,参数量由7012825变为3604758,模型大小由14.4 M变为7.5 M,说明了改进算法对夜间行人识别的有效性。
Aiming at the problems of slow speed,low precision and poor recognition effect in the process of traditional nighttime pedestrian recognition,an improved YOLOv5s nighttime pedestrian recognition algorithm was proposed.Firstly,C3CSGC module was used to replace C3 module in the original YOLOv5s network model.Secondly,the loss function CIoU of YOLOv5s is replaced by EIoU.Finally,the feature pyramid of YOLOv5s model is replaced by weighted bidirectional feature pyramid BiFPN.Experimental results show that for the improved nighttime pedestrian recognition algorithm,the Precision(P)and Recall(R)of the original YOLOv5s model are increased by 4.1%and 5.9%,and the values of mAP_0.5 is increased by 7.2%,respectively.The number of parameters changed from 7012825 to 3604758,and the model size changed from 14.4 M to 7.5M,indicating the effectiveness of the improved algorithm for nighttime pedestrian recognition.
作者
刘文骄
廖义奎
梅欢子
胡昌瑞
徐钲槟
Liu Wenjiao;Liao Yikui;Mei Huanzi;Hu Changrui;Xu Zhengbing(College of Electronic Information,Guangxi Minzu University,Nanning 530006,China)
出处
《现代计算机》
2024年第18期1-7,76,共8页
Modern Computer
