A lightweight vehicle detection network fusing feature pyramid and channel attention

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, this paper proposes a fast and accurate vehicle detector termed as MCE-SSD. 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 BiFPN idea is used to construct a weighted bi-directional fusion network CBiFPN to obtain multi-dimensional vehicle features, while introducing ECA-Net in the feature extraction layer to re-calibrate the importance of different feature channels and further improve the model performance. In the end, CIoU is introduced to better regress the bounding box of the target vehicle, and DIoU-NMS is used to solve the problem of error suppression for dense targets. Compared with SSD, our proposed MCE-SSD improves mAP by 8.30% 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.