LEPD-Net: A Lightweight and Efficient Network for Pedestrian Detection

The pedestrian detection is crucial in practical applications, such as autonomous driving and video surveillance. However, the existing research mainly focuses on improving detection accuracy, with relatively little attention paid to model complexity and operational efficiency. In scenarios with high real-time requirements, the practical deployment of pedestrian detectors still faces many difficulties. To this end, we propose a lightweight and efficient pedestrian detection network (LEPD-Net). First, we design a PoolFormer-based detection head (PDH) to reduce the model computation and inference time. Second, to compensate for the deficiency of PDH in global context modeling, we design a triple-branch joint attention module (TJAM). TJAM uses only a small number of parameters and strengthens the model's contextual representation by capturing spatial location dependencies and global semantic information between channels. Finally, after incorporating PDH and TJAM into the backbone network, a lightweight and efficient pedestrian detector is constructed. We benchmarked the model on mainstream pedestrian datasets Caltech and CityPersons. The results show that our model achieves the current state-of-the-art performance level. In addition, our model reduces inference time by 25% while maintaining accuracy.