TY  - JOUR
T1  - Crowd Detection: Leveraging YOLO for Human Recognition
AU  - Yiğit, Gülsüm
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.31127/tuje.1627839
JF  - Turkish Journal of Engineering
JO  - TUJE
PB  - Murat YAKAR
WT  - DergiPark
SN  - 2587-1366
SP  - 571
EP  - 577
VL  - 9
IS  - 3
LA  - en
AB  - Human detection in crowded environments is essential for applications such as surveillance, autonomous navigation, and crowd management. This study examines the performance of various YOLO (You Only Look Once) models in detecting humans. We combined four public human detection datasets to create a comprehensive dataset for crowd detection. Experiments were conducted on YOLOv5, YOLOv8, and YOLOv11 models, employing different architectures and model sizes. Performance was evaluated using mean Average Precision (mAP) at Intersection over Union (IoU) thresholds of 50% (mAP@50) and across 50-95% (mAP@50-95). The results indicate that the YOLOv8m model achieved the highest mAP@50 of 0.944 and mAP@50-95 of 0.697, surpassing larger models such as YOLOv11x, which attained 0.90 and 0.632 respectively. Additionally, other YOLOv8 variants demonstrated superior or comparable performance to their YOLOv5 and YOLOv11 counterparts. These findings highlight the effectiveness of YOLOv8’s optimized structures in delivering accurate and efficient human detection in high-density settings.
KW  - Human Detection
KW  - Yolo Models
KW  - Crowd Detection
KW  - Object Detection
KW  - Mean Average Precision
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CR  - Redmon, J. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/ISCAS.2008.4542023.
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CR  - Zhan, W., Sun, C., Wang, M., She, J., Zhang, Y., Zhang, Z., & Sun, Y. (2022). An improved YOLOv5 real-time detection method for small objects captured by UAV. Soft Computing, 26, 361–373. https://doi.org/10.1007/s00500-021-06407-8
CR  - Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976.https://doi.org/10.48550/arXiv.2209.02976
CR  - Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464–7475). https://doi.org/10.1109/CVPR52729.2023.00721.
CR  - Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458. https://doi.org/10.48550/arXiv.2405.14458.
CR  - Gülgün, O. D., & Erol, H. (2020). Classification performance comparisons of deep learning models in pneumonia diagnosis using chest X-ray images. Turkish Journal of Engineering, 4(3), 129–141. https://doi.org/10.31127/tuje.652358.
CR  - Aydın, V. A. (2024). Comparison of CNN-based methods for yoga pose classification. Turkish Journal of Engineering, 8(1), 65–75. https://doi.org/10.31127/tuje.1275826
CR  - Polater, S. N., & Sevli, O. (2024). Deep learning based classification for Alzheimer’s disease detection using MRI images. Turkish Journal of Engineering, 8(4), 729–740. https://doi.org/10.31127/tuje.1434866.
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CR  - Oghaz, M. M., Khadka, A. R., Argyriou, V., & Remagnino, P. (2019). Content-aware density map for crowd counting and density estimation. arXiv preprint arXiv:1906.07258. 
https://doi.org/10.48550/arXiv.1906.07258.
CR  - Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X. (2015). Deep people counting in extremely dense crowds. In Proceedings of the 23rd ACM International Conference on Multimedia (pp. 1299–1302). https://doi.org/10.1145/2733373.280633.
CR  - Alhawsawi, A. N., Khan, S. D., & Rehman, F. U. (2024). Crowd counting in diverse environments using a deep routing mechanism informed by crowd density levels. Information, 15(5), 275. https://doi.org/10.3390/info15050275.
CR  - Yang, G., & Zhu, D. (2023). Survey on algorithms of people counting in dense crowd and crowd density estimation. Multimedia Tools and Applications, 82(9), 13637–13648. https://doi.org/10.1007/s11042-022-13957-y.
CR  - Yao, H., Han, K., Wan, W., & Hou, L. (2017). Deep spatial regression model for image crowd counting. arXiv preprint arXiv:1710.09757. https://doi.org/10.48550/arXiv.1710.09757.
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CR  - Zhang, L., Lin, L., Liang, X., & He, K. (2016). Is Faster R-CNN doing well for pedestrian detection? In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 (pp. 443–457). Springer. https://doi.org/10.1007/978-3-319-46475-6_28.
CR  - Sindagi, V. A., & Patel, V. M. (2018). A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recognition Letters, 107, 3–16. https://doi.org/10.1016/j.patrec.2017.07.007.
CR  - Ali, M. A., Hussain, A. J., & Sadiq, A. T. (2022). Detection and count of human bodies in a crowd scene based on enhancement features by using the YOLOv5 algorithm. Iraqi Journal of Computers, Communications, Control and Systems Engineering, 22(2), 125–134. https://doi.org/10.33103/uot.ijccce.22.2.11.
CR  - Elshahawy, M., Aseeri, A. O., El-Sappagh, S., Soliman, H., Elmogy, M., & Abu-Elkheir, M. (2022). Identification and classification of crowd activities. CMC-Computers, Materials & Continua, 72(1), 815–832. https://doi.org/10.32604/cmc.2022.023852.
CR  - People detection dataset. Retrieved December 24, 2024, from https://universe.roboflow.com/hcl-ca18b/people-detection-yrmsh
CR  - People dataset. Retrieved December 24, 2024, from https://universe.roboflow.com/capstone-ssbpj/people-fycjl
CR  - Solar system dataset. Retrieved December 24, 2024, from https://universe.roboflow.com/human-dataset-v2/solar-system
CR  - People detection dataset. Retrieved December 24, 2024, from https://universe.roboflow.com/jmedel/people-detection-f0fgt
CR  - People detection combined dataset. Retrieved December 24, 2024, from https://app.roboflow.com/crowd-counting-bsg6a/people-detection-combined-dataset
UR  - https://doi.org/10.31127/tuje.1627839
L1  - https://dergipark.org.tr/en/download/article-file/4556064
ER  -