汽车工程学报2026，Vol.16Issue(5)：770-778,9.DOI:10.3969/j.issn.2095‒1469.2026.05.05
集成多场景的2D与3D融合人员检测方法研究
Multi-Scenario Occupant Detection and Recognition Based on 2D and 3D Data Fusion
摘要
Abstract
To meet the demand for multi-scenario detection in smart cockpits,this paper proposes a method that integrates 2D and 3D data fusion to achieve in-vehicle occupant detection,distracted driving recognition,and face recognition.The method uses three-channel Red-Green-Blue(RGB)images,Infrared(IR),and Depth data acquired by a Stellar 200 3D camera as input.Task-specific algorithms based on deep convolutional neural networks are developed for different scenarios.The MobileNet-V2 is employed as the classification network for distracted driving detection,and the model is trained using both IR images and fused IR-Depth inputs.For left-behind occupant detection,YOLOv5s first identifies human targets,Multi-Task Convolutional Neural Network(MTCNN)then detects faces,and Age Classification CNN(Age-CNN)extracts age features to determine the type of occupant left behind.For face recognition,the MTCNN algorithm detects faces in the video stream in real time,and the lightweight MobileFaceNet extracts high-dimensional facial embeddings for accurate identity matching.Real-vehicle validation results demonstrate the high accuracy of the proposed method.By fusing multimodal data,the method effectively reduces the impact of changes in the external environment on detection performance.关键词
车内生命体遗留/分心驾驶/人脸身份识别/卷积神经网络Key words
residual life forms in the car/distracted driving/facial identification/convolutional neural network分类
信息技术与安全科学引用本文复制引用
张海洋,杨云霄,曹立波,赵诚,安先龙,高超..集成多场景的2D与3D融合人员检测方法研究[J].汽车工程学报,2026,16(5):770-778,9.基金项目
湖南大学整车先进设计制造技术全国重点实验室自主研究课题资助项目(72475001) （72475001）
