Vehicle detection is an important supporting technology for the realization of intelligent transportation, autonomous driving, etc. Poor accuracy or low inference vehicle detectors are limited in application, therefore this paper proposes a fast and accurate vehicle detector. First, the front-end feature extraction network VGG16 is replaced by MobileNetV3_Large, which reduces the number of parameters and computation, and increases the ability to extract high-dimensional features. Next, the feature pyramid idea is used to construct a weighted bi-directional fusion network to obtain multi-dimensional vehicle features; In the end, introducing efficient channel attention in the feature extraction layer to re-calibrate the importance of different feature channels and further improve the model performance. Compared with SSD, our proposed model improves mAP by 7.50% and 3.50% on KITTI dataset and BDD100K dataset, and with real-time inference (more than 40 FPS), it reports a better trade-off in terms of detection accuracy and speed, illustrating the effectiveness of our method.