双目视觉的智能汽车目标检测算法研究  

Research on intelligent vehicle target detection algorithm based on binocular vision

作　　者：申彩英[1] 朱思瑶 黄兴驰 SHEN Caiying;ZHU Siyao;HUANG Xingchi(Automobile and Traffic Engineering,Liaoning University of Technology,Jinzhou 121001,China)

机构地区：[1]辽宁工业大学汽车与交通工程学院,辽宁锦州121001

出　　处：《重庆理工大学学报(自然科学)》2023年第11期11-19,共9页Journal of Chongqing University of Technology:Natural Science

基　　金：国家自然科学基金面上项目(51675257);辽宁省教育厅项目(LJKMZ20220978)。

摘　　要：智能汽车的环境感知是实现自动驾驶的重要一环,对道路交通主要参与者(汽车、行人、骑行者)进行检测识别的研究。提出一种基于YOLO算法改进的端到端的目标检测算法dual-YOLO。将注意力机制引入检测网络模型,提高网络对有效特征的学习权重,从而提高检测精度。加入双目摄像头距离测算模块,获取目标距离信息。测试结果表明:dual-YOLO目标检测算法识别道路交通主要参与者的平均准确率能达到85.99%,在骑行者和行人检测方面明显优于其他算法,检测速度能达到60 fps,提出的算法能较好地完成智能汽车行驶实时检测和测距需求。The environmental perception of intelligent vehicles is an important part of achieving autonomous driving,and object detection is one of the crucial functions of the environmental perception systems.Accurate and rapid detection of targets nearby is essentail for the smart vehicles.The faster and more accurately the detection algorithm can detect surrounding targets,the better.However,the detection speed and accuracy of existing perception algorithms still need improving.The distance information between the surrounding objects and the vehicle itself during its operation should also be obtained.The widely used YOLO series of algorithms,as representatives of one-stage object detection methods,have achieved a satisfactory balance between speed and detection accuracy,but there is still great potential for further improvement.This paper proposes an end-to-end object detection algorithm based on YOLO algorithm improvement-dual YOLO,which detects and measures the major participants in road traffic(cars,pedestrians,cyclists).The attention mechanism acts in the same way as humans observe things,focusing on more important information.The basic idea of combining attention mechanism with convolutional neural networks is to allow the network to concentrate on effective feature information in the image and ignore invalid information,thus improving the overall detection precision.Its mechanism of action is achieved by changing the weights of neurons in the network.This paper is based on the improvement of YOLOV5 algorithm,adding a channel attention mechanism to the backbone network to increase the learning weight of the neural network for effective features,thereby enhancing detection accuracy.In the meantime,a distance measurement module for binocular cameras is introduced to obtain target distance information.The basic principle of binocular camera ranging is triangulation,which employs the visual difference between two cameras to determine the distance of an object.Firstly,the camera is calibrated and distortion is corrected,

关 键 词：智能驾驶汽车 双目视觉 神经网络 目标检测 注意力机制 

分 类 号：U463.6[机械工程—车辆工程] TP391.41[交通运输工程—载运工具运用工程]