Energy Aware Femto Object Detection on Embedded Drone Vision Processors During Flight
DOI:
https://doi.org/10.66238/47mpx354Keywords:
Femto-Object Detection, Embedded Vision Processors, Unmanned Aerial Vehicles, Energy EfficiencyAbstract
The proliferation of unmanned aerial vehicles has catalyzed rapid advancements in autonomous navigation and aerial surveillance. However, detecting exceptionally small entities, categorized as femto-objects, from high altitudes remains a formidable computational challenge. These objects often occupy fewer than a dozen pixels in a high-resolution image, demanding intense computational resources for feature extraction and classification. Compounding this challenge is the stringent energy constraint inherent to battery-powered drones. This paper presents a comprehensive investigation into energy-aware femto-object detection strategies tailored specifically for embedded vision processors. By integrating hardware-software co-design principles, we explore methodologies that dynamically balance detection accuracy with energy consumption. Our analysis encompasses dynamic resolution scaling, precision quantization, and context-aware region proposals designed to minimize unnecessary memory accesses and arithmetic operations. The study evaluates the trade-offs between thermal design power limits and inference latency, demonstrating that intelligent power management algorithms can significantly extend flight times without critically compromising the reliability of femto-object recognition. Through extensive architectural simulations and algorithmic profiling, this research provides a foundational framework for deploying highly efficient, scalable, and responsive vision systems on edge-computing platforms for next-generation aerial robotics.
References
1. Zhao, Haoyu, et al. "LSTD: Long Short-Term Temporal Diffusion for Video Generation." IEEE Transactions on Multimedia (2026).
2. Yang, S., Liu, F., Chen, D., Huang, H., & Zhou, J. (2024). Few-shot classification model compression via school learning. IEEE Transactions on Circuits and Systems for Video Technology, 34(12), 12244-12257.
3. Zhang, J., Luo, H., Yang, H., Xu, X., Wu, Q., Shi, Y., ... & Wang, J. (2023). Neuraldome: A neural modeling pipeline on multi-view human-object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8834-8845).
4. Li, Q., Liu, Z., Xu, T., Luo, T., & Hou, C. (2026). Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classification. arXiv preprint arXiv:2601.05785.
5. Chu, Y. B., Li, J., Jia, P., Cui, J., Zhang, R., Kang, X., Lv, M., & Zhang, S. (2021). Irf1- and Egr1-activated transcription plays a key role in macrophage polarization: A multiomics sequencing study with partial validation. International immunopharmacology, 99, 108072. https://doi.org/10.1016/j.intimp.2021.108072
6. Ma, Y., Qu, D., & Pyrozhenko, M. (2026). Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence. Biomimetics, 11(1), 48.
7. Zhang, Jiaquan, et al. "Lightweight llm agent memory with small language models." arXiv preprint arXiv:2604.07798 (2026).
8. Guo, H., Lu, C., Bao, F., Pang, T., Yan, S., Du, C., & Li, C. (2023). Gaussian mixture solvers for diffusion models. Advances in Neural Information Processing Systems, 36, 25598-25626.
9. Liu, C., Ma, L., Zhang, X. T., Zhang, Y., Zhang, H., Yang, X., & Tian, F. (2026). Boosting omni-modal language models: Staged post-training with visually debiased evaluation. arXiv preprint arXiv:2605.12034.
10. Tu, P., Huang, Y., Zheng, F., He, Z., Cao, L., & Shao, L. (2022, June). Guidedmix-net: Semi-supervised semantic segmentation by using labeled images as reference. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 2, pp. 2379-2387).
11. Qu, W., Wang, J., Gong, Y., Huang, X., & Xiao, L. (2025). An end-to-end robust point cloud semantic segmentation network with single-step conditional diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 27325-27335).
12. Zhao, H., Qi, Z., Wang, C., Zheng, Q., Lu, G., Chen, F., ... & Wu, Z. (2025). Dynamictrl: Rethinking the basic structure and the role of text for high-quality human image animation. arXiv preprint arXiv:2503.21246.
13. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
14. Zhang, B., Wang, X., Kao, H., Zhang, G., Gao, C., Wang, Y., ... & Chen, K. (2026). Consensus Matrix: A Role-Specialized Multi-Agent Framework for Structured Collaborative Decision-Making in Agentic Visual Media Workflows. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4613-4622).
15. Wang, C., Ke, L., Liu, X., Huang, D., Yao, J., & Chu, W. Explainable AI Analysis of Brake Control in CARLA Through Reference and Distilled Policies. Frontiers in Robotics and AI, 13, 1847808.
16. Qi, Z., Yuan, Y., Ruan, X., Wang, S., Zhang, W., & Huang, Q. (2024). Collaborative debias strategy for temporal sentence grounding in video. IEEE Transactions on Circuits and Systems for Video Technology, 34(11), 10972-10986.
17. Li, Z., Bai, X., Zhang, T., Shen, P., Xu, W., & Peng, Y. (2025, October). Enhanced Velocity Field Modeling for Gaussian Video Reconstruction. In 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 304-314). IEEE.
18. Zou, Fanglei, et al. "Enhancing cyclodextrin glycosyltransferase-mediated potato starch modification via plasma-activated water and twin-screw extrusion treatment." Food Chemistry 491 (2025): 145247.
19. Zhao, H., Wang, Q., Zhan, G., Min, W., Zou, Y., & Cui, S. (2022). Need only one more point (NOOMP): Perspective adaptation crowd counting in complex scenes. IEEE Transactions on Multimedia, 25, 1414-1426.
20. Tang, A., Hiraoka, T., Hiraoka, N., Shi, F., Kawaharazuka, K., Kojima, K., ... & Inaba, M. (2024, May). Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 13107-13114). IEEE.
21. Lv, Q., Kong, W., Li, H., Zeng, J., Qiu, Z., Qu, D., ... & Pang, J. (2025). F1: A vision-language-action model bridging understanding and generation to actions. arXiv preprint arXiv:2509.06951.
22. Peng, Q., Zheng, C., & Chen, C. (2024). A dual-augmentor framework for domain generalization in 3d human pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2240-2249).
23. Xiao, Q., & Zhou, K. (2026). MACD: Model-Aware Contrastive Decoding via Counterfactual Data. arXiv preprint arXiv:2602.01740.
24. Huang, Z., Wang, J., Chen, L., Xiao, B., Cai, L., Zeng, Y., & Xu, J. (2025, October). MVISU-Bench: Benchmarking Mobile Agents for Real-World Tasks by Multi-App, Vague, Interactive, Single-App and Unethical Instructions. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 8797-8805).
25. Liu, Y., Liu, H., Wang, H., & Liu, M. (2022). Regularizing visual semantic embedding with contrastive learning for image-text matching. IEEE Signal Processing Letters, 29, 1332-1336.
26. Zhao, C., Zhang, J., Du, J., Shan, Z., Wang, J., Yu, J., ... & Xu, L. (2024). I'm hoi: Inertia-aware monocular capture of 3d human-object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 729-741).
27. Li, Q., Luo, T., & Liao, J. (2025). Theory-inspired deep multi-view multi-label learning with incomplete views and noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20706-20715).
28. Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approachusing Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.
29. Xu, W., Huang, H., Gong, Y., Yu, L., Wu, Q., & Zhang, J. (2025). Hierarchical multi-prototype discrimination: Boosting support-query matching for few-shot segmentation. IEEE Transactions on Multimedia.
30. Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024). A conditional denoising diffusion probabilistic model for point cloud upsampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20786-20795).
31. Tao, Xiaoyu, et al. "A structural roadmap to gut health: A comprehensive review of edible insect chitin–protein complexes as alternative protein-derived, structure-specific prebiotics." International Journal of Biological Macromolecules (2026): 151312.
32. Chen, L., Jiang, X., Liu, X., & Zhou, Z. (2020). Robust low-rank tensor recovery via nonconvex singular value minimization. IEEE Transactions on Image Processing, 29, 9044-9059.
33. Zhang, W., & Pei, S. (2026). Predictive prefetching for retrieval-augmented generation. Proceedings of Machine Learning Research. The Forty-third International Conference on Machine Learning, ICML 2026. https://par.nsf.gov/biblio/10686293
34. Li, Q., Liu, Z., Luo, W., Luo, T., & Hou, C. (2026). Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory. arXiv preprint arXiv:2605.24602.
35. Qu, D., & Ma, Y. (2025). Magnet-bn: markov-guided Bayesian neural networks for calibrated long-horizon sequence forecasting and community tracking. Mathematics, 13(17), 2740.
36. Z. Qi et al., "Self-Regulated Learning for Egocentric Video Activity Anticipation" in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 45, no. 06, pp. 6715-6730, June 2023, doi: 10.1109/TPAMI.2021.3059923.
37. Li, Q., Luo, T., Jiang, M., Liao, J., & Jiang, Z. (2024, October). Deep incomplete multi-view network semi-supervised multi-label learning with unbiased loss. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 9048-9056).
38. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2025). Distinct patterns in neuromuscular adaptation to repeated perturbations in chronic ankle instability. Journal of NeuroEngineering and Rehabilitation.
39. Huang, H., Zhang, J., Zhang, J., Xu, J., & Wu, Q. (2020). Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification. IEEE Transactions on Multimedia, 23, 1666-1680.
40. Zhou, Yufan, et al. "Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos." arXiv preprint arXiv:2507.03393 (2025).
41. Mi, L., Wang, W., Tu, W., He, Q., Kong, R., Fang, X., ... & Liu, Y. (2025, March). Empower vision applications with LoRA LMM. In Proceedings of the Twentieth European Conference on Computer Systems (pp. 261-277).
42. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
43. Liu, Y., Feng, W., Liu, Z., Huang, S., & Lv, J. (2025). Aligning information capacity between vision and language via dense-to-sparse feature distillation for image-text matching. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 21679-21688).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Clara Murray (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the International Journal of Machine Learning and Engineering (IJMLE) are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, reproduction, adaptation, and commercial use in any medium or format, provided the original author(s) and the source are properly credited.
Authors retain the copyright of their work while granting the journal the right of first publication.
To view a copy of this license, please visit:
https://creativecommons.org/licenses/by/4.0/
