文档介绍：计算机工程与应用
Computer Engineering and Applications
ISSN 1002-8331,CN 11-2127/TP
;轻量化;安全帽佩戴识别;Ghost模块
文献标志码: A 中图分类号: doi：.1002--0357
Lightweight network models and applications for identifying helmet wear
HU Wenjun, YANG Liqiong, XIAO Yufeng, HE Hongsen
School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621010,
China
Abstract: Helmet wearing recognition is a target detection task with less classification. The existing large-scale deep
learning network model with high accuracy is used to identify helmet wearing, which has problems of parameter re-
dundancy and large calculation, which is not suitable for deployment in embedded devices with limited computation to
adapt to the actual site environment. To solve these problems, a lightweight network model YOLO-Ghost-BiFPNs3
suitable for embedded devices is proposed. On the basis of YOLOv4, a new network structure is reconstructed based on
Ghost module, and the depth and width of the network are trimmed. BiFPNs3, a lightweight module based on weighted
channel addition, is designed to replace the FPN+PAN structure which has a large amount of calculation. A more quan-
tifiable H-Swish activatio