This is an outdated version published on 2024-03-13. Read the most recent version.

YOLO architectures comparison for urban cyclist detection in an autonomous driving environment

Authors

Mauricio Arias-Correa Instituto Tecnológico Metropolitano https://orcid.org/0000-0003-0619-235X
Jorge Alexander David Rodríguez Instituto Tecnológico Metropolitano https://orcid.org/0009-0002-6718-9955
Mateo Quintero Restrepo Instituto Tecnológico Metropolitano https://orcid.org/0009-0002-3138-4409
Paula Alejandra Ortiz Santana Instituto Tecnológico Metropolitano https://orcid.org/0009-0004-0480-7423
Luis Miguel Gómez Meneses Instituto Tecnológico Metropolitano https://orcid.org/0000-0002-0667-7472

DOI:

https://doi.org/10.24054/rcta.v1i43.2820

Keywords:

Yolo, VRU, deep learning, cyclists' detection, autonomous vehicle

Abstract

The World Health Organization (WHO) states that over 55% of road traffic accident fatalities involve vulnerable road users, including 3% who are cyclists. While autonomous vehicles are capable of detecting objects and individuals on roadways, the detection of cyclists and the prediction of their movements continue to pose significant challenges. This paper presents results from the comparison of YOLOv7, YOLOv8, and YOLO-NAS architectures for urban cyclist detection. The methodology ensures that the detectors were trained under the same conditions. Subsequently, they were evaluated using 111 cyclist images with metrics such as IoU, precision, and recall. The results highlight advantages and disadvantages within each architecture, suggesting a priority for either inference time or the quality of cyclist detection in future work.

Downloads

Download data is not yet available.

References

Flohr, F. B. (2018). Vulnerable Road User Detection and Orientation Estimation for Context-Aware Automated Driving.

Thrun, S. (2010). Toward robotic cars. Communications of the ACM, 53(4), 99–106. https://doi.org/10.1145/1721654.1721679

Alhajyaseen, W. K. M., Asano, M., & Nakamura, H. (2012). Estimation of left-turning vehicle maneuvers for the assessment of pedestrian safety at intersections. IATSS Research, 36(1), 66–74. https://doi.org/10.1016/j.iatssr.2012.03.002

Brohm, T., Haupt, K., & Thiel, R. (2019). Pedestrian Intention and Gesture Classification Using Neural Networks. ATZ Worldwide, 121(4), 26–31. https://doi.org/10.1007/s38311-019-0006-6

Chen, Y. Y., Jhong, S. Y., Li, G. Y., & Chen, P. H. (2019). Thermal-Based Pedestrian Detection Using Faster R-CNN and Region Decomposition Branch. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, 2019–2020. https://doi.org/10.1109/ISPACS48206.2019.8986298

Heo, D., Nam, J. Y., & Ko, B. C. (2019). Estimation of pedestrian pose orientation using soft target training based on teacher-student framework. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051147

Lan, W., Dang, J., Wang, Y., & Wang, S. (2018). Pedestrian detection based on yolo network model. Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, 1547–1551. https://doi.org/10.1109/ICMA.2018.8484698

Murphey, Y. L., Liu, C., Tayyab, M., & Narayan, D. (2018). Accurate pedestrian path prediction using neural networks. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-Janua, 1–7. https://doi.org/10.1109/SSCI.2017.8285398

Fairley, P. (2017). Self-driving cars have a bicycle problem [News]. IEEE Spectrum, 54(3), 12–13.

Mannion, P. (2019). Vulnerable road user detection: state-of-the-art and open challenges. 1–5. http://arxiv.org/abs/1902.03601

Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., & Gavrila, D. M. (2016). A new benchmark for vision-based cyclist detection. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus(Iv), 1028–1033. https://doi.org/10.1109/IVS.2016.7535515

Kress, V., Jung, J., Zernetsch, S., Doll, K., & Sick, B. (2019). Pose Based Start Intention Detection of Cyclists. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2381–2386. https://doi.org/10.1109/ITSC.2019.8917215

Garcia-Venegas, M., Mercado-Ravell, D. A., Pinedo-Sanchez, L. A., & Carballo-Monsivais, C. A. (2021). On the safety of vulnerable road users by cyclist detection and tracking. Machine Vision and Applications, 32(5), 109.

Casas, E., Ramos, L., Bendek, E., & Rivas-Echeverría, F. (2023). Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection. IEEE Access.

BITZI, software image scraping. (2017). Instituto Tecnológico Metropolitano. Medellín. https://doi.org/10.1109/mspec.2017.7864743

Shijie, J., Ping, W., Peiyi, J., & Siping, H. (2017, October). Research on data augmentation for image classification based on convolution neural networks. In 2017 Chinese automation congress (CAC) (pp. 4165-4170). IEEE.

Wickramanayake, S., Hsu, W., & Lee, M. L. (2021). Explanation-based data augmentation for image classification. Advances in Neural Information Processing Systems, 34, 20929-20940.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).Clymer, J. R. (1992). “Discrete Event Fuzzy Airport Control”. IEEE Trans. On Systems, Man, and Cybernetics, Vol. 22, No. 2.

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).

Yasir, M., Zhan, L., Liu, S., Wan, J., Hossain, M. S., Isiacik Colak, A. T., ... & Yang, Q. (2023). Instance segmentation ship detection based on improved Yolov7 using complex background SAR images. Frontiers in Marine Science, 10, 1113669.

Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics. URL: https://github. com/ultralytics/ultralytics.

Xia, K., Lv, Z., Zhou, C., Gu, G., Zhao, Z., Liu, K., & Li, Z. (2023). Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection. Sensors, 23(11), 5114.

Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Tan, K. C. (2021). A survey on evolutionary neural architecture search. IEEE transactions on neural networks and learning systems.

Padilla, R., Netto, S. L., & Da Silva, E. A. (2020, July). A survey on performance metrics for object-detection algorithms. In 2020 international conference on systems, signals and image processing (IWSSIP) (pp. 237-242). IEEE.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.

Li, X., Li, L., Flohr, F., Wang, J., Xiong, H., Bernhard, M., Pan, S., Gavrila, D. M., & Li, K. (2017). A unified framework for concurrent pedestrian and cyclist detection. IEEE Transactions on Intelligent Transportation Systems, 18(2), 269–281. https://doi.org/10.1109/TITS.2016.2567418

Lin, Y., Wang, P., & Ma, M. (2017). Intelligent Transportation System(ITS): Concept, Challenge and Opportunity. Proceedings - 3rd IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2017, 3rd IEEE International Conference on High Performance and Smart Computing, HPSC 2017 and 2nd IEEE International Conference on Intelligent Data and Securit, 167–172. https://doi.org/10.1109/BigDataSecurity.2017.50

World Health Organization. (2018). Global status report on road safety 2018. In Global status report on road safety 2018: Summary (No. WHO/NMH/NVI/18.20).

Downloads

Additional Files

Resumen.mp3 (Español (España))

Published

2024-03-13 — Updated on 2024-03-13

Versions

How to Cite

[1]

M. Arias-Correa, J. A. David Rodríguez, M. Quintero Restrepo, P. A. Ortiz Santana, and L. M. Gómez Meneses, “YOLO architectures comparison for urban cyclist detection in an autonomous driving environment”, RCTA, vol. 1, no. 43, pp. 64–72, Mar. 2024.