Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-877-2025
https://doi.org/10.5194/ms-16-877-2025
Research article
 | 
21 Nov 2025
Research article |  | 21 Nov 2025

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

Cui Chen, Jun Li, Zequn Shuai, Yiyun Wang, and Yaohong Wang

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Cited articles

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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S.: End-to-end object detection with transformers, European Conference on Computer Vision, Springer International Publishing, 213–229, https://doi.org/10.1007/978-3-030-58452-8_13, 2020. a, b
Chen, G., Choi, W., Yu, X., Han, T., and Chandraker, M.: Learning efficient object detection models with knowledge distillation, Advances in Neural Information Processing Systems, 30, https://dl.acm.org/doi/10.5555/3294771.3294842, 2017. a
Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., and Sun, J.: DetNAS: Backbone search for object detection, Advances in Neural Information Processing Systems, 32, https://dl.acm.org/doi/10.5555/3454287.3454883, 2019. a
Choi, J. D. and Kim, M. Y.: A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection, ICT Express, 9, 222–227, https://doi.org/10.1016/j.icte.2021.12.016, 2023. a
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Short summary
We developed a fast and compact model to detect pedestrians in crowded scenes, especially when people are partly hidden or far away. Our method improves how the model learns from small and difficult cases, and how it balances speed and accuracy. It runs much faster than current systems while maintaining similar accuracy, making it suitable for real-time use on mobile and edge devices.
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