Aiming at the problem of insufficient real-time recognition capabilities of existing vehicle recognition methods, a single shot multibox detector(SSD) algorithm based on improved depthwise separable convolution is proposed for vehicle type recognition. Firstly, this paper proposes to extract the features using depthwise separable convolution network, and introduces the inverted residuals module to solve the problem of reduced accuracy due to the small number of channels and feature compression. Secondly, based on the rigid body characteristics of the vehicles, the region candidate frame is redesigned to reduce the amount of model parameter calculation. Finally, ablation experiments are performed on the BIT-Vehicle dataset to compare the performance differences of different network models. The results show that the improved depthwise separable convolution SSD vehicle type recognition method can achieve a recognition accuracy of 96.12%, and the detection speed increases to 0.078 s/frame.