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Updated: Jun 18, 2025

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CBAM VGG16:使用CBAM嵌入VGG16架构进行高效的驾驶员分心分类.

Chittathuru Himala Praharsha¹, Alwin Poulose¹

¹School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Thiruvananthapuram 695551, Kerala, India.

Computers in biology and medicine

|August 2, 2024

概括

一个新的CBAM VGG16深度学习模型显著改善了自动驾驶系统的驾驶员分心分类. 这种增强的模型通过从摄像头数据准确识别驾驶员活动来提高安全性.

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科学领域:

计算机视觉和机器学习
自主驾驶系统自主驾驶系统
人工智能用于汽车安全

背景情况:

驾驶员监控系统 (DMS) 对自动驾驶系统 (ADS) 至关重要,以确保驾驶员和车辆的安全.
分类驾驶员的分心对于ADS中的实时安全增强至关重要.
由于人类驾驶行为的不可预测性,准确地分类驾驶员分心是具有挑战性的.

研究的目的:

提出一种新的深度学习架构,CBAM VGG16,用于改进驾驶员分心的分类.
通过集成卷积块注意模块 (CBAM) 来增强VGG16模型的特征提取能力.
评估拟议的CBAM VGG16模型与现有的最先进架构的性能.

主要方法:

通过在VGG16架构中嵌入CBAM层来开发混合深度学习模型.
在开罗美国大学 (AUC) 训练并测试了CBAM VGG16模型,分心驾驶员数据集版本2 (AUCD2).
与其他模型 (如DenseNet121,Xception,MoblieNetV2,InceptionV3和VGG16.6) 进行分类性能指标 (准确性,损失,精度,F1得分,回忆,混矩阵) 的比较.

主要成果:

拟议的CBAM VGG16实现了高分类准确度:AUCD2数据集上的摄像头1的98.65%和摄像头2的97.85%.

关键词:

注意模块的注意力模块.自动驾驶系统 (ADS) 是一种自动驾驶系统.自动驾驶汽车是什么意思卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.司机分心的分类司机分心的分类驾驶员监控系统驾驶员监控系统图像的分类图像的分类.视觉几何组 (VGG) 是一个视觉几何组.

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与传统VGG16型号相比,显著提高了性能,相对相机1和相机2的性能分别提高了3.7%和5%.

与VGG16单独相比,Grad-CAM可视化证实,CBAM层可以提高分散注意力的图像中的关键区域的注意力.

结论:

CBAM VGG16架构有效地提高了针对驾驶员分心的特征提取和分类性能.
拟议的模型在驾驶员分心分类任务中胜过了几种最先进的深度学习架构.
该研究验证了CBAM集成和数据增强技术在ADS中强大的DMS的有效性.