MAPD: multi-receptive field and attention mechanism for multispectral pedestrian detection.

For pedestrian detection in all weather conditions, multispectral imagery is the preferred solution for multimodal data acquisition. Due to the complementarity of multispectral data, the performance of pedestrian detection has been continuously improved. However, it is precise because of the diversity of data that the complexity of the model is increased. How to obtain a simple and lightweight high-performance network is the primary problem that the industry needs to solve. To solve this problem, this paper firstly proposes a lightweight high-performance network multi-receptive field and attention mechanism for multispectral pedestrian detection (MAPD). MAPD is a new scalable network based on multi-receptive field and attention mechanism. It cleverly combines multi-receptive field module and convolutional block attention module (CBAM) attention module to obtain multi-receptive field and attention module (MA). The module can be easily embedded into other network structures. After that, we analyze the proportion of pedestrian objects in the image to determine the receptive field range of the target, and use this range to design the multi-receptive field module of the network to obtain a network model suitable for detecting different object tasks. The MAPD network proposed in this paper has a very low parameter amount. Ablation studies on KAIST and CVC-14 datasets show that our method is effective and achieves state-of-the-art detection performance. [ABSTRACT FROM AUTHOR]