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YOLOv5s-FAC: enhanced feature association detector for person-vehicle counting in smart park

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Abstract

Currently, tracking by detection technique is widely used in crowd or vehicle counting. However, it is difficult to meet detection and counting with mixed pedestrian and vehicular traffic of smart park scenes in emergencies, for occluding small objects recognition problem. An improved detection model YOLOv5-FAC is proposed based YOLOv5s. First, a P2 detection layer is added to expand the detection range of the model and improve its detection ability of different sizes. Second, an auxiliary inference network is constructed using programmable gradient information to provide the model with a stronger information fitting capability. Finally, a cascading triplet attention mechanism is added to the head of model to increase the feature fusion capability. Then, a collision line counting method combined with OCSORT track technology is proposed, in which both direction movement of traffic is considered. The experimental results show that YOLOv5s-FAC has a significantly improved detection quality, with a mAP of 69.5%, and the counting accuracy reached 94.4%.

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Data availability

The data and code of this study are openly available in [zenodo] at https://doi.org/https://doi.org/10.5281/zenodo.11509921.

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Acknowledgements

The authors would like to acknowledge the financial support provided by the National Key Research and Development Program of China under Grant 2023YFC3008904 and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture under Grant X20109.

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Contributions

Wei-Guang Zou: algorithm improvement experiments, including validation, ablation, and comparison experiments, implementation of counting methods, writing of the original manuscript, and editing. Yu-ling Hu: methodology and reviews. Xin-Yi Wang: comparative experiments of object tracking, image data acquisition for counting scenarios, and validation of counting performance. Jia-Feng Li: framework construction for detection and tracking, expansion and collection of Person and Vehicle datasets, and analysis of experimental data.

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Correspondence to YuLing Hu.

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Zou, W., Hu, Y., Wang, X. et al. YOLOv5s-FAC: enhanced feature association detector for person-vehicle counting in smart park. SIViP 19, 62 (2025). https://doi.org/10.1007/s11760-024-03735-8

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