Given low vehicle detection accuracy and serious miss detection in a haze environment, a vehicle detection algorithm with multi-scale feature fusion in a haze environment is proposed. Firstly, the conditional generation and adversarial network is employed to preprocess the haze images. Then, as the object feature is not obvious in a haze environment, a multi-scale feature fusion module is put forward. On the basis of YOLOv3, a shallow branch is added for upsampling splicing and fusing with deep layer features during extracting features from backbone networks. As a result, the feature map with the scale of 104×104 is obtained, which is adopted to enhance the shallow semantic information. The feature enhancement strategy guided by the CBAM attention mechanism is utilized to ensure the integrity of context information and improve detection accuracy. Finally, the dehazed images are sent to the improved YOLOv3 network for detection. Experimental results show that the proposed algorithm has better performance than the YOLOv3 algorithm on the RTTS dataset. The proposed model can achieve an average accuracy of 81% and a recall of 67.52% and can locate vehicles more accurately.