Aiming at the problem that the model parameters of the current mainstream target detection algorithms are too large and cannot be transplanted to mobile devices and applied to assisted driving, this paper proposes an improved YOLOv5s target detection algorithm. Firstly, CSPDarknet, the backbone network of YOLOv5s algorithm, is replaced by MobileNet-V3, a lightweight network model, which solves the problem of large network model and many parameters, reduces the network depth and improves the data inference speed. Secondly, a weighted bidirectional feature pyramid structure Bi-FPN is used to enhance feature extraction, and multi-scale features are integrated to expand the receptive field. Finally, the loss function is optimized and CIoU is used as the boundary box regression loss function to improve the slow convergence speed of the original GIoU model, so that the prediction box is more consistent with the real box, and at the same time reduce the difficulty of network training. Experimental results show that compared with YOLOv5s, SSD, YOLOv3 and YOLOv4_tiny, the mAP of the improved algorithm on KITTI dataset is improved by 4.4, 15.7, 12.4 and 19.6, respectively. Compared with YOLOv5s and lightweight network YOLOv4_tiny, the model size is reduced by 32.4 MB and 21 MB respectively, and the detection speed is improved by 17.6% and 43% respectively. The improved algorithm meets the requirements of small model and high accuracy, and provides a solution for improving detection speed and accuracy of road target detection in assisted driving.