Fig. 1. Characteristic of pedestrian in U-FOV infrared images. (a) Large and medium scale pedestrians; (b) small scale pedestrians

Fig. 2. Architecture of multi-scale infrared pedestrian detection network based on Darknet53

Fig. 3. Architecture of attention module

Fig. 4. Residual module

Fig. 5. Principle of pedestrian detection

Fig. 6. Learning rate and loss curves. (a) Learning rate on Caltech dataset; (b) loss on Caltech dataset; (c) learning rate on U-FOV dataset; (d) loss on U-FOV dataset

Fig. 7. Salient coefficient and feature maps

Fig. 8. Distribution of pedestrian size in U-FOV test set

Fig. 9. Visualization results of infrared pedestrian detection

Fig. 10. P-R curves under different IoU thresholds. (a) IoU threshold is 0.3; (b) IoU threshold is 0.45; (c) IoU threshold is 0.5; (d) IoU threshold is 0.7

Fig. 11. Visualization results of infrared pedestrian detection on LTIR dataset at different scenes