摘要
分心驾驶是交通事故的主要诱因之一,为提高分心驾驶行为检测的速度和精度,提出一种基于姿态辅助的轻量化驾驶行为检测网络。针对特征提取质量不佳的问题,设计了一种高效的大核自注意力机制,增强捕捉局部和全局特征的能力,以提取丰富的低层特征。同时,将分组卷积与胶囊网络结合,以提取驾驶行为的语义特征,在保证高精度的条件下,减少模型参数量。此外,引入姿态估计作为辅助,进一步提升了网络在复杂背景下的检测准确性。实验结果表明:笔者方法在SFD和AUC两个基准数据集上分别取得了99.71%和95.38%的准确率,与当前的先进模型相比,在保持相同准确率的情况下,参数量仅为0.29 M(减少了61.8%),在吞吐量为801张图像每秒的服务器中推理速度约为2.57 ms;提出的基于姿态辅助的轻量化驾驶行为检测网络能够取得较高的准确率,且参数量满足嵌入式设备要求,能为安全行车提供支持。
Distracted driving is one of the primary causes of traffic accidents.To improve the speed and accuracy of detecting distracted driving behaviors,a lightweight driving behavior detection network based on pose assistance was proposed.To address the issue of poor feature extraction quality,an efficient large-kernel self-attention mechanism was designed to enhance the ability to capture both local and global features,thereby extracting rich low-level features.Meanwhile,grouped convolution was combined with capsule network to extract semantic features of driving behavior,which reduced the amount of model parameters while ensuring high accuracy.Furthermore,the detection accuracy of network in complex backgrounds was further improved by introducing pose estimation as an auxiliary.Experiment results show that the proposed method achieves accuracy of 99.71%and 95.38%on the SFD and AUC benchmark datasets,respectively;compared to current advanced models,while maintaining the same accuracy,the parameter count is only 0.29M(a reduction of 61.8%),and the inference speed in a server with a throughput of 801 images/s is about 2.57ms.The proposed lightweight driving behavior detection network based on attitude assistance achieves relatively high accuracy and its parameter amount satisfies the demands of embedded devices,providing support for safe driving.
作者
蓝章礼
范亮
张洪
唐若瀚
徐元通
黄大荣
LAN Zhangli;FAN Liang;ZHANG Hong;TANG Ruohan;XU Yuantong;HUANG Darong(School of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074,China;State Key Laboratory of Mountain Bridge and Tunnel Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Artificial Intelligence,Anhui University,Heifei 230601,Anhui,China)
出处
《重庆交通大学学报(自然科学版)》
北大核心
2025年第8期75-82,共8页
Journal of Chongqing Jiaotong University(Natural Science)
基金
国家自然科学基金项目(52278291)
重庆市研究生联合培养基地建设项目(JDLHPYJD2023004)
重庆交通大学研究生科研创新项目(2024s0107)。
关键词
交通运输工程
姿态辅助
驾驶行为检测
轻量化
胶囊网络
traffic and transportation engineering
attitude assistance
driver behaviour detection
lightweight
capsule network
