Regarding to the problems of low detection accuracy， poor real-time performance， and missed detection of small target vehicles in existing road vehicle detection and recognition algorithms， a road vehicle detection and recognition algorithm based on densely connected convolutional neural networks was proposed. Firstly， Based on YOLOv4 （You Only Look Once version 4） network framework， by adopting the densely connected deep residual network structure， the feature reuse in the feature extraction stage was strengthened to realize the use of features with lower complexity on shallow layers. Then， a jump connection structure was integrated to the multi-scale feature fusion network to strengthen the feature information fusion and expression capability of the network， which reduced the missed detection rate of vehicles. Finally， the dimensional clustering algorithm was used to recalculate the anchor sizes， which were allocated to different detection scales according to a reasonable strategy. Experimental results show that the proposed algorithm achieves the detection accuracy of 98.21% and the detection speed of 48.05 frame/s on KITTI dataset， and it also has a good detection effect for vehicles in the complex and harsh environment of Berkeley DeepDrive （BDD100K） dataset， ensuring required real-time performance and effective accuracy improvement.