Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-371-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
RIL-YOLO: a lightweight real-time object detection model on mobile devices for kart racing
Cited articles
Anggrainy, R., Yoga, N. G., Wiyono, A., Putri, D. M., Wahyudi, Z. T., and Septiyan, Y. A.: Unveiling the Future of Safety: Cutting-Edge Simulation Testing of e-Kart Bumpers, J. Phys. Conf. Ser., 012096, https://doi.org/10.1088/1742-6596/2866/1/012096, 2024.
Chen, C., Li, J., Shuai, Z., Wang, Y., and Wang, Y.: A lightweight optimization framework for real-time pedestrian detection in dense and occluded scenes, Mech. Sci., 16, 877–886, https://doi.org/10.5194/ms-16-877-2025, 2025.
Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., and Chan, S.-H. G.: Run, don't walk: chasing higher FLOPS for faster neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.03667, 2023.
Ding, X., Zhang, X., Han, J., and Ding, G.: Diverse branch block: Building a convolution as an inception-like unit, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20–25 June 2021, IEEE, 10881–10890, https://doi.org/10.1109/CVPR46437.2021.01074, 2021.
Kaur, P., Khehra, B. S., and Mavi, E. B. S.: Data augmentation for object detection: A review, in: 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 537–543, https://doi.org/10.1109/MWSCAS47672.2021.9531849, 2021.