Title: Pedestrian head detection based on improved YOLOv5

Authors: Yong Ren; Tian Qiu; Jian Shen

Addresses: Engineering School, Applied Technology College of Soochow University, Suzhou, China ' Engineering School, Applied Technology College of Soochow University, Suzhou, China ' Engineering School, Applied Technology College of Soochow University, Suzhou, China

Abstract: This paper presents an improved YOLOv5 model for the detection of pedestrian heads in crowded scenes. By incorporating FasterNet, the C2f module, Soft-NMS and optimal transport assignment (OTA), the proposed model achieves significant performance improvements over the baseline YOLOv5s model, with a recall of 75.21%, AP50 of 84.31%, and AP50-95 of 57.29%, while maintaining a reduced computational complexity of 14.2 GFLOPs. In comparison with other YOLO series models, the proposed model demonstrates a higher AP50-95 score while maintaining competitive recall and AP50 values. The effectiveness of the model has been demonstrated in diverse scenarios, including various crowd densities, lighting conditions, pedestrian orientations, image resolutions, and pedestrian sizes. The results indicate that the improved YOLOv5 model exhibits robustness, adaptability, and generalisation capabilities in challenging pedestrian head detection tasks.

Keywords: pedestrian head detection; YOLOv5; FasterNet; soft non-maximum suppression; Soft-NMS; optimal transport assignment; OTA.

DOI: 10.1504/IJCSE.2025.149760

International Journal of Computational Science and Engineering, 2025 Vol.28 No.6, pp.595 - 606

Received: 30 Apr 2023
Accepted: 14 May 2024
Published online: 12 Nov 2025 *

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