A lightweight optimization framework for real-Time pedestrian detection in dense and occluded scenes

Abstract

Pedestrian detection is one of the most widely applied tasks in industrial computer vision. It encapsulates three core challenges of object detection: detecting small objects, handling heavy occlusion, and balancing speed and accuracy for deployment on mobile devices. In targeting scenarios relating to the Internet of Things (IoT), we propose a dedicated lightweight pedestrian detector that is robust to occlusions. First, we redesign the decoupled prediction head with a hierarchical structure, separating classification confidence estimation from bounding box regression. We then decode the offsets from the regression branch, extract features from high-confidence predictions, and fuse these with classification feature maps to enhance the local reliability of semantic features. Furthermore, we introduce a label-dynamic matching strategy that increases the number of high-quality positive samples, particularly improving matching for small and occluded objects. Finally, an optimized knowledge distillation framework significantly boosts the prediction accuracy of the compact model, facilitating deployment on edge devices. Experimental results on the CrowdHuman test set show that our proposed approach achieves comparable accuracy to the baseline (53.8 %) with an inference latency of only 7.1 ms-281.7 % faster than the baseline.