Oriented Object Detection: Edge Models vs. Cloud Architectures
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13834Keywords:
Machine learning, Image processing, Flying robots, Perception and sensing, Embedded computer control systems and applications, UAVsAbstract
Aerial object detection from UAV imagery requires models capable of localizing objects with arbitrary orientations while operating under the computational constraints of onboard processing. This work evaluates eleven oriented object detection models from the YOLO-OBB, RTMDet, RoI Transformer and Strip R-CNN families. The comparison considers both detection accuracy and deployment-oriented metrics, including latency, throughput, model size and computational complexity. The evaluation uses overlapping image patches from the DOTA v1.0 dataset. Models are compared in terms of mAP and weighted F1 using confidence thresholds selected on the calibration subset. The results show that high-capacity models such as YOLOv11x-OBB and RTMDet-l are the most suitable candidates for cloud-side inference, whereas the YOLO nano variants and RTMDet-tiny offer different trade-offs for edge-side processing. Based on these findings, a hybrid edge–cloud architecture is proposed, where lightweight onboard detectors process the UAV video stream in real time and more accurate cloud-side models are reserved for ambiguous, low-confidence or high-priority detections.
References
Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., Yao, Y., 2024. Poly kernel inception network for remote sensing detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 27706–27716.
Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, Q., 2019. Learning RoI transformer for oriented object detection in aerial images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2849–2858.
Ding, J., Xue, N., Xia, G.-S., Bai, X., Yang, W., Yang, M. Y., Belongie, S., Luo, J., Datcu, M., Pelillo, M., et al., 2021. Object detection in aerial images: A large-scale benchmark and challenges. IEEE transactions on pattern analysis and machine intelligence 44 (11), 7778–7796.
Gill, S. S., Golec, M., Hu, J., Xu, M., Du, J., Wu, H., Walia, G. K., Murugesan, S. S., Ali, B., Kumar, M., et al., 2025. Edge AI: A taxonomy, systematic review and future directions. Cluster Computing 28 (1), 18.
Li, Y., Hou, Q., Zheng, Z., Cheng, M.-M., Yang, J., Li, X., 2023. Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 16794–16805.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll´ar, P., 2017. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988.
Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., Chen, K., 2022. RTMDet: An empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784.
NVIDIA, 2026. NVIDIA TensorRT Documentation. https://docs.nvidia.com/deeplearning/tensorrt/latest/index.html, accessed: 2026-05-30.
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.
Reilly, V., Idrees, H., Shah, M., 2010. Detection and tracking of large number of targets in wide area surveillance. In: European conference on computer vision. Springer, pp. 186–199.
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.
Sadgrove, E. J., Falzon, G., Miron, D., Lamb, D. W., 2018. Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM). Computers in Industry 98, 183–191.
Tang, G., Ni, J., Zhao, Y., Gu, Y., Cao,W., 2023. A survey of object detection for uavs based on deep learning. Remote Sensing 16 (1), 149. Ultralytics, 2026. Ultralytics documentation. https://docs.ultralytics.com/, accessed: 2026-05-30.
Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L., 2018. DOTA: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer visión and pattern recognition. pp. 3974–3983.
Xu, Y., Fu, M., Wang, Q., Wang, Y., Chen, K., Xia, G.-S., Bai, X., 2020. Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE transactions on pattern analysis and machine intelligence 43 (4), 1452–1459.
Yuan, X., Zheng, Z., Li, Y., Liu, X., Liu, L., Li, X., Hou, Q., Cheng, M.-M., 2026. Strip R-CNN: Large strip convolution for remote sensing object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 40. pp. 12259–12267.
Yuan, Y., Gao, S., Zhang, Z.,Wang,W., Xu, Z., Liu, Z., 2024. Edge-cloud collaborative UAV object detection: Edge-embedded lightweight algorithm design and task offloading using fuzzy neural network. IEEE Transactions on Cloud Computing 12 (1), 306–318.
Zhou, Y., Yang, X., Zhang, G., Wang, J., Liu, Y., Hou, L., Jiang, X., Liu, X., Yan, J., Lyu, C., et al., 2022. MMRotate: A rotated object detection benchmark using pytorch. In: Proceedings of the 30th ACM international conference on multimedia. pp. 7331–7334.
Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., Jiao, J., 2015. Orientation robust object detection in aerial images using deep convolutional neural network. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp. 3735–3739.
Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., 2018. Vision meets drones: A challenge. arXiv preprint arXiv:1804.07437.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eri Pérez Corral, Enrique Alegre Gutiérrez, Christopher Gaul, Milad Mirjalili, Waqar Tanveer
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
