INTEGRATING FEDERATED LEARNING AND YOLOV11 FOR OBJECT DETECTION IN AUTONOMOUS VEHICLES
Autonomous vehicles (AVs) require high-performance systems for environmental perception, especially for accurate object detection. Systems based on deep neural networks perform well, but often face challenges due to the high computational cost and the need for centralised data collection. This paper proposes the integration of federated learning with the YOLOv11 model to create more efficient and scalable ATS solutions. The FLYolo11 model is presented, which optimises object detection in computationally constrained environments, improving accuracy and performance without the need for centralised training. Experimental results show that the model significantly improves the detection performance at average computational cost compared to other approaches.
Tikhonov M.K. Integrating federated learning and YOLOv11 for object detection in autonomous vehicles // Research result. Information technologies. – Т.9, №4, 2024. – P. 58-64. DOI: 10.18413/2518-1092-2024-9-4-0-7
While nobody left any comments to this publication.
You can be first.
1. Ultralytics YOLO Docs. – URL: https://docs.ultralytics.com/ru (circulation date 24.11.2024).
2. Liu W. et al. Ssd: Single shot multibox detector // Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. – Springer International Publishing, 2016. – pp. 21-37.
3. Ren S. et al. Faster R-CNN: Towards real-time object detection with region proposal networks // IEEE transactions on pattern analysis and machine intelligence. – 2016. – Т. 39. – №. 6. – pp. 1137-1149.
4. Ross T.Y., Dollár G. Focal loss for dense object detection //proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – pp. 2980-2988.
5. Wang S. et al. Federated deep learning meets autonomous vehicle perception: Design and verification // IEEE network. – 2022. – Т. 37. – №. 3. – pp. 16-25.
6. McMahan B. et al. Communication-efficient learning of deep networks from decentralized data // Artificial intelligence and statistics. – PMLR, 2017. – pp. 1273-1282.
7. Niranjan D. R. et al. Performance Analysis of SSD and Faster RCNN Multi-class Object Detection Model for Autonomous Driving Vehicle Research Using CARLA Simulator // 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). – IEEE, 2021. – pp. 1-6.
8. Dosovitskiy A. et al. CARLA: An open urban driving simulator // Conference on robot learning. – PMLR, 2017. – С. 1-16.
9. Manikandan N. S., Ganesan K. Deep learning based automatic video annotation tool for self-driving car // arXiv preprint arXiv:1904.12618. – 2019.
10. Wang S. et al. Edge federated learning via unit-modulus over-the-air computation // IEEE Transactions on Communications. – 2022. – Т. 70. – №. 5. – pp. 3141-3156.