In order to improve the detection effect of the vehicle assisted driving system on the vehicle ahead,and further obtain accurate distance information,this paper proposes an improved you only look once v5s (YOLOv5s) target vehicle detection algorithm,and uses binocular to measure the distance of the vehicle ahead.Based on the YOLOv5s detection network,firstly,the convolutional block attention module (CBAM) is introduced into the network to effectively extract the contour features of the detection target;secondly,the PANet network in Neck is replaced with BiFPN to improve the feature fusion ability,and DIoU is used to optimize the loss function to enhance the accuracy of vehicle detection.The SURF algorithm is used for stereo matching,and the feature matching points are constrained to obtain the optimal disparity value.Finally,the distance information of the preceding vehicle is obtained through the principle of binocular vision ranging.The test shows that within a distance of 20 m,the accuracy of vehicle recognition rate is 92.1%,increased by 1.54%,and the average error rate of ranging is 2.75%.