YOLOV10 vs. YOLOV8: Performance Improvements for Vehicle Detection at Multilane Roundabouts
By: Khaled Hamad and Lubna Obaid
References
- Al Mudawi, N., Qureshi, A. M., Abdelhaq, M., Alshahrani, A., Alazeb, A., Alonazi, M. and Algarni, A. (2023) Vehicle detection and classification via YOLOv8 and deep belief network over aerial image sequences. Sustainability, 15(19). DOI:10.3390/su151914597.
- Ammar, A., Koubaa, A., Ahmed, M. and Saad, A. (2019) Aerial images processing for car detection using convolutional neural networks: Comparison between Faster R-CNN and YoloV3. ArXiv, 1–28. DOI:10.20944/preprints201910.0195.v1.
- Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A. and Ouni, K. (2019) Car detection using unmanned aerial vehicles: Comparison between Faster R-CNN and YOLOv3. In: Proceedings of the 1st International Conference on Unmanned Vehicle Systems-Oman, UVS 2019, Muscat, February 2019. IEEE, pp. 1–6. DOI:10.1109/UVS.2019.8658300.
- Benjdira, B., Koubaa, A., Taher, A., Khan, Z., Ammar, A. and Boulila, W. (2022) TAU: A framework for video-based traffic analytics leveraging artificial intelligence and unmanned aerial systems. Engineering Applications of Artificial Intelligence, 114, 105095. DOI:10.1016/j.engappai.2022.105095.
- Bhavsar, Y. M., Zaveri, M. S., Raval, M. S. and Zaveri, S. B. (2023) Vision-based investigation of road traffic and violations at urban roundabout in India using UAV video: A case study. Transportation Engineering, 14, 100207. DOI:10.1016/j.treng.2023.100207.
- Biyik, M. Y., Atik, M. E. and Duran, Z. (2023) Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis. International Journal of Engineering and Geosciences, 8(2), 138–145. DOI:10.26833/ijeg.1080624.
- Bozcan, I. and Kayacan, E. (2020) AU-AIR: A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance. In: Proceedings of IEEE International Conference on Robotics and Automation, Paris, May - August 2020. IEEE, pp. 8504–8510. DOI: 10.1109/ICRA40945.2020.9196845.
- Duman, Z. N., Çulcu, M. B. and Katar, O. (2022) YOLOv5-based vehicle objects detection using UAV images. Turkish Journal of Forecasting, 06(1), 40–45. DOI:10.34110/forecasting.1145381
- Feng, R., Fan, C., Li, Z. and Chen, X. (2020) Mixed road user trajectory extraction from moving aerial videos based on convolution neural network detection. IEEE Access, 8, 43508–43519. DOI:10.1109/ACCESS.2020.2976890.
- Geetha, A. S., Al, M., Alif, R., Hussain, M. and Allen, P. (2024) Comparative analysis of YOLOv8 and YOLOv10 in vehicle detection: Performance metrics and model efficacy. Vehicles, 6(3), 1364–1382. DOI: 10.3390/vehicles6030065.
- Hussain, M. (2024) YOLOv5, YOLOv8 and YOLOv10: The go-to detectors for real-time vision. ArXiv, 1–12. http://arxiv.org/abs/2407.02988.
- Karna, N. B. A., Putra, M. A. P., Rachmawati, S. M., Abisado, M. and Sampedro, G. A. (2023) Toward accurate fused deposition modeling 3D printer fault detection using improved YOLOv8 with hyperparameter optimization. IEEE Access, 11, 74251–74262. DOI:10.1109/ACCESS.2023.3293056.
- Liu, S., Wang, S., Shi, W., Liu, H., Li, Z. and Mao, T. (2019) Vehicle tracking by detection in UAV aerial video. Science China Information Sciences, 62, 1–3. DOI:10.1007/s11432-018-9590-5.
- Masuduzzaman, M., Islam, A., Sadia, K. and Young, S. (2022) UAV-based MEC-assisted automated traffic management scheme using blockchain. Future Generation Computer Systems, 134, 256–270. DOI:10.1016/j.future.2022.04.018.
- Ngoc, H. T., Nguyen, K. H., Hua, H. K., Nguyen, H. V. N. and Quach, L. D. (2023) Optimizing YOLO performance for traffic light detection and end-to-end steering control for autonomous vehicles in Gazebo-ROS2. International Journal of Advanced Computer Science and Applications, 14(7), 475–484. DOI:10.14569/IJACSA.2023.0140752.
- Rosende, S. B., Ghisler, S. and Fern, J. (2022) Dataset: Traffic images captured from UAVs for use in training machine vision algorithms for traffic management. Data, 7(5), 53. DOI:10.3390/data7050053.
- Saetchnikov, I., Skakun, V. and Tcherniavskaia, E. (2021) Efficient objects tracking from an unmanned aerial vehicle. In: Proceedings of IEEE 8th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Naples, June 2021. IEEE, pp. 221–225. DOI:10.1109/MetroAeroSpace51421.2021.9511748.
- Sapkota, R., Meng, Z., Ahmed, D., Churuvija, M., Du, X., Ma, Z. and Karkee, M. (2024) Comprehensive performance evaluation of YOLOv10, YOLOv9 and YOLOv8 on detecting and counting fruitlet in complex orchard environments. DOI:10.48550/arXiv.2407.12040.
- Sary, I. P., Andromeda, S. and Armin, E. U. (2023) Performance comparison of YOLOv5 and YOLOv8 architectures in human detection using aerial images. Ultima Computing: Jurnal Sistem Komputer, 15(1), 8–13. DOI:10.31937/sk.v15i1.3204.
- Shahbazi, M., Simeonova, S., Lichti, D. and Wang, J. (2020) Vehicle tracking and speed estimation from unmanned aerial videos. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 623–630. DOI:10.5194/isprs-archives-XLIII-B2-2020-623-2020.
- Sohan, M., Sai Ram, T. and Rami Reddy, C. V. (2024) A review on YOLOv8 and its advancements. In: Proceedings of International Conference on Data Intelligence and Cognitive Informatics, Tirunelveli, June 2023.Springer, pp. 529–545. DOI:10.1007/978-981-99-7962-2_39.
- Solimani, F., Cardellicchio, A., Dimauro, G., Petrozza, A., Summerer, S., Cellini, F. and Renò, . (2024) Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity. Computers and Electronics in Agriculture, 218. DOI:10.1016/j.compag.2024.108728.
- Terven, J. (2023) A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680–1716. DOI:10.3390/make5040083.
- Terven, J. and Cordova-Esparza, D.-M. (2023) A comprehensive review of YOLO: From YOLOv1 and Beyond. DOI:10.48550/arXiv.2304.00501.
- Wang, G., Bochkovskiy, A. and Jocher, G. (2023) Ultralytics YOLOv8: Cutting-edge real-time object detection. Available at: https://github.com/ultralytics/YOLOv8.
- Wang, G., Chen, Y., An, P., Hong, H., Hu, J. and Huang, T. (2023) UAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors, 23(16). DOI:10.3390/s23167190.
- Wang, J., Simeonova, S. and Shahbazi, M. (2019) Orientation- and scale-invariant multi-vehicle detection and tracking from unmanned aerial videos. Remote Sensing, 11(18), 2155. DOI:10.3390/rs11182155.
Language: English
Page range: 164 - 178
Published on: Apr 26, 2026
Published by: Transport and Telecommunication Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2026 Khaled Hamad, Lubna Obaid, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.