哈尔滨商业大学学报（自然科学版）2023，Vol.39Issue(6)：676-684,9.
基于改进卷积神经网络的安全带佩戴识别
Seat belt-wearing recognition based on improved convolutional neural network
摘要
Abstract
Aiming at the requirement that drivers and passengers need to wear seat belts when a motor vehicle is running,this paper proposed a 33-layer convolutional neural network model for seat belt-wearing recognition.The convolutional algorithm,pooling algorithm,and network layer connection mode of a convolutional neural network were introduced,and the network structure was designed.Aiming at the problem of insufficient accuracy and stability of the existing optimization algorithms,an AWM optimization algorithm integrating the classical momentum idea was proposed.AWM optimization algorithm was used to optimize and train the parameters of the network based on two types of data sets of passengers wearing seat belts and not wearing seat belts,and the RIVNet model was obtained.The experimental results showed that the RIVNet model could improve the accuracy of seat belt-wearing detection,and could efficiently process data and extract image features.Based on this model,a vehicle seat belt-wearing recognition system was developed based on the target detection algorithm Faster R-CNN.关键词
卷积神经网络/网络结构/优化算法/Faster R-CNN/汽车安全带佩戴识别系统Key words
convolutional neural network/network structure/optimization algorithm/Faster R-CNN/vehicle seat belt-wearing recognition system分类
信息技术与安全科学引用本文复制引用
丁家益,周跃进..基于改进卷积神经网络的安全带佩戴识别[J].哈尔滨商业大学学报（自然科学版）,2023,39(6):676-684,9.基金项目
深部煤矿采动响应与灾害防控国家重点实验室基金资助项目(SKLMRDPC22KF03). （SKLMRDPC22KF03）
