Combining event cameras with traditional cameras for vehicle target detection can not only solve the problems of over-exposure， underexposure， and motion blur in high dynamic range of traditional cameras， but also solve the problem of low detection accuracy caused by missing texture information of event cameras. Existing fusion algorithms often have problems such as high computational complexity， loss of feature information， and poor fusion results. To solve the above problems， a vehicle target detection algorithm that effectively fused event cameras and conventional cameras was proposed. Firstly， a spatio-temporal event representation based on Event Frequency （EF） and Time Surface （TS） was proposed， which encoded event data into event frames. Then， a Feature fusion module based on Channel and Spatial Attention mechanism （FCSA） was proposed to perform feature-level fusion of image frames and event frames. Finally， the prior box was optimized by using the differential evolution search algorithm to further improve the vehicle detection performance. In addition， due to the lack of public datasets containing image frames and event data， a vehicle detection dataset MVSEC-CAR was established. The experimental results show that， on the public PKU-DDD17-CAR dataset， the mean Average Precision （mAP） of the proposed algorithm is 2.6 percentage points higher than that of the second best ADF （Attention fusion Detection Framework）， and it achieves a higher frame rate， effectively improving the accuracy of vehicle target detection and robustness to lighting， which validate the effectiveness of the proposed event representation， feature fusion， and prior box optimization algorithms.