School of Information Science and Engineering,NingboTech University
随着电动自行车骑行人员必须佩戴头盔的法规出台，相关智能视觉检测技术的需求也应运而生，本文以交通监控视频图像中骑行人员佩戴头盔情况为研究对象，以YOLO目标检测框架为基础，首先采用了分支吸收模块改善残差骨干网络, 然后通过结构通道重组提升卷积层特征融合, 最后应用设计的结构融合剪枝进一步压缩模型超参数。实验结果表明, 该算法具有较优的精度和实时性, 小目标检测效果也较好, 其多分类平均精度达到88.8%，检测速度可达29.5fps, 基本满足交通视频监控的需求。
With the regulations of wearing helmets while driving the electric bicycle, it is urgent to develop a detection algorithm that can accurately detect whether the drivers are wearing helmets. This paper introduces a novel method to detect the helmets based on the YOLO framework. The branch absorption module is proposed to improve the residual backbone network, then the feature fusion is improved through the channel recombination. Finally, the designed structural fusion pruning is applied to further compress the hyper-parameters of the model. The experimental results showed that, the proposed algorithm has higher accuracy and faster speed. Performance of small targets detection also can be improved, with the average accuracy of multiple classification up to 88.8% and detection speed of 29.5fps, which can meet the demand of video surveillance in real applications.
Sun Yi, Wu Siman, Fang Wei, et al. Object Detection of Security Monitoring Based on ResNet[J]. Journal of Integration Technology.