A lightweight and explainable model for driver abnormal behavior recognition

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Jingbin Hao , Xiaokai Sun , Xinhua Liu , Dezheng Hua , Jianhua Hu
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引用次数: 0

Abstract

With the advancement of intelligent transportation systems, accurate identification of driver abnormal behavior is crucial for enhancing road safety. However, the limited computing power of vehicular systems poses a challenge for running efficient and explainable behavior recognition models. This paper proposes a lightweight and explainable driver abnormal behavior recognition model based on an improved You Only Look Once version 8 (YOLOv8). Firstly, a Spatial and Channel Reconstruction Convolution (SCConv) module is introduced to optimize the Convolution to Feature (C2f) structure, enhancing the model's feature extraction capabilities while reducing parameter redundancy. Secondly, a Spatial Pyramid Pooling with Fast Large Separable Kernel Attention (SPPF-LSKA) module is designed to better capture image context and integrate global information. Additionally, a Dynamic upsample (Dysample) module is introduced to improve the model's ability to capture subtle driver movements. Lastly, a Lightweight Shared Group Normalization Convolution Detection Head (LSGCDH) is designed to enhance the model's generalization ability, significantly reducing the model's computational load, parameter count, and size. Experimental results demonstrate that our approach has significant advantages for edge device deployment compared to mainstream algorithms. The visualization results effectively corroborate the role of each improved structure, enhancing the explainability of the abnormal behavior recognition model, which is beneficial for deployment in vehicular systems and contributes to improving road traffic safety.
用于识别驾驶员异常行为的轻量级可解释模型
随着智能交通系统的发展,准确识别驾驶员的异常行为对提高道路安全至关重要。然而,车辆系统的计算能力有限,这对运行高效且可解释的行为识别模型提出了挑战。本文基于改进的 You Only Look Once version 8(YOLOv8),提出了一种轻量级、可解释的驾驶员异常行为识别模型。首先,引入空间和通道重构卷积(SCConv)模块,优化卷积到特征(C2f)结构,增强模型的特征提取能力,同时减少参数冗余。其次,为了更好地捕捉图像上下文并整合全局信息,设计了空间金字塔池化与快速大型可分离内核关注(SPPF-LSKA)模块。此外,还引入了动态上采样(Dysample)模块,以提高模型捕捉驾驶员细微动作的能力。最后,我们还设计了一个轻量级共享组归一化卷积检测头(LSGCDH),以增强模型的泛化能力,从而显著降低模型的计算负荷、参数数量和大小。实验结果表明,与主流算法相比,我们的方法在边缘设备部署方面具有显著优势。可视化结果有效地证实了每个改进结构的作用,增强了异常行为识别模型的可解释性,有利于在车辆系统中的部署,有助于提高道路交通安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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