Pedestrian detection based on channel feature fusion and enhanced semantic segmentation.

At present, pedestrian detection is widely applied to autonomous driving and intelligent transportation and robots, etc. But the balance between accuracy and speed is still not reached. In complex background with high pedestrian density and serious occlusion, missing detection or false detection may occur by pedestrian detection models based on center and scale prediction (CSP). An improved pedestrian detection method based on channel feature fusion and enhanced semantic segmentation is presented. A feature fusion module based on squeeze and excitation is proposed in feature extraction. Multi-scale feature maps are fused to obtain faster detection speed and higher detection accuracy. An enhanced semantic segmentation module is presented in detection head to solve missing detection for long-distance pedestrians. CIOU (Complete Intersection Over Union) loss function is used to improve the confidence levels of pedestrians. Experiments on different networks, scales of feature fusion and detection methods are carried out to verify the performance of proposed approach. The experimental results show that the proposed model can detect pedestrians with high accuracy in occluded, dense and long-distance scenes. The detection speed can be accelerated while keeping low missed detection rate and less computational cost. It is shown that the approach can achieve high accuracy and robustness especially in complex background. [ABSTRACT FROM AUTHOR]