A Review of Techniques for Distinguishing Tiny Objects by Drones in Complex Environments and Real-Time Applications
Keywords:
Drones, Deep learning, Feature extrication, Tiny object recognition, UAV, YOLOAbstract
Drones have accomplished innumerable applications in Defense, Agriculture, Search and Rescue, Surveillance, Delivery, Logistics, Emergency Management, etc. due to their inexpensiveness, ease of excess, potential for vertical takeoff and landing and ability to employ in perilous or remote areas. UAV technology is beneficial in security surveillance zones. Drones have also become essential in modern warfare, and their succession towards prominent autonomy is inexorable. If Artificial Intelligence (AI) techniques are incorporated into Drones, it aids the real-time analysis, autonomous performance, and precisely distinguishing objects. AI algorithms for navigation aid UAVs to fly without human assistance, lowering the possibility of mishaps brought on by human mistake. Because AI algorithms are capable of object detection and classification, UAVs have ability to locate and follow items of interest, including people, cars, and animals. UAVs can identify and examine variations, structures and in the environment with the aid of AI systems that can evaluate images and videos. However, upgrading the precision of object recognition algorithms and obtaining a suitable dataset for the specific problem needs to be primarily considered. This paper imparts a comprehensive overview of the remotely sensed object detection techniques in Drones under various scenarios of blurred images, earthquake assessment and real-time monitoring.
References
M. Lee, M. Choi, T. Yang, “A Study on the Advancement of Intelligent Military Drones: Focusing on Reconnaissance Operations,” IEEE access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3390035.
M. M. Quamar, B. Al-Ramadan, K. Khan, M. Shafiullah, and S. E. Ferik, “Advancements and Applications of Drone-Integrated Geographic Information System Technology—A Review,” Remote Sensing, vol. 15, no. 5039, p. 5039, Oct. 2023, doi: https://doi.org/10.3390/rs15205039.
K. Telli et al., “A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs),” Systems, vol. 11, no. 8, p. 400, Aug. 2023, doi: https://doi.org/10.3390/systems11080400.
H. Jahani, Y. Khosravi, B. Kargar, K.-L. Ong, and S. Arisian, “Exploring the role of drones and UAVs in logistics and supply chain management: a novel text-based literature review,” International Journal of Production Research, vol. 63, no. 5, pp. 1–25, Jul. 2024, doi: https://doi.org/10.1080/00207543.2024.2373425.
R. A. Kemarau , “Global perspectives on unmanned aerial vehicles technology in social sciences: applications, innovations, and future research directions,” Geocarto International, vol. 39, no. 1, Jan. 2024, doi: https://doi.org/10.1080/10106049.2024.2413547.
F. Ahmed, J. C. Mohanta, A. Keshari, and P. S. Yadav, “Recent Advances in Unmanned Aerial Vehicles: A Review,” Arabian Journal for Science and Engineering, vol. 47, Apr. 2022, doi: https://doi.org/10.1007/s13369-022-06738-0.
E. Yucesoy, E. EcemBalcik, and E. Coban, “The role of drones in disaster response: A literature review of operations research applications,” International Transactions in Operational Research, Jun. 2024, doi: https://doi.org/10.1111/itor.13484.
D. Tezza and M. Andujar, “The State-of-the-Art of Human–Drone Interaction: A Survey,” IEEE Access, vol. 7, pp. 167438–167454, 2019, doi: https://doi.org/10.1109/access.2019.2953900.
S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan, “Unmanned Aerial Vehicles (UAVs): Practical aspects, applications, Open challenges, Security issues, and Future Trends,” Intelligent Service Robotics, vol. 16, no. 1, Jan. 2023, doi: https://doi.org/10.1007/s11370-022-00452-4.
S. Jobaer, X. Tang, Y. Zhang, G. Li, and F. Ahmed, “A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images,” Complex & Intelligent Systems, vol. 11, no. 1, Dec. 2024, doi: https://doi.org/10.1007/s40747-024-01676-w.
J. Wang, F. Shao, X. He, and G. Lu, “A Novel Method of Small Object Detection in UAV Remote Sensing Images Based on Feature Alignment of Candidate Regions,” Drones, vol. 6, no. 10, pp. 292–292, Oct. 2022, doi: https://doi.org/10.3390/drones6100292.
H. H. Helgesen, T. H. Bryne, E. F. Wilthil, and T. A. Johansen, “Camera-Based Tracking of Floating Objects using Fixed-wing UAVs,” Journal of Intelligent & Robotic Systems, vol. 102, no. 4, Jul. 2021, doi: https://doi.org/10.1007/s10846-021-01432-z.
H. Mahmoud, I. F. Kurniawan, A. Aneiba, and A. T. Asyhari, “Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR,” Photonirvachak, May 2024, doi: https://doi.org/10.1007/s12524-024-01869-3.
F. Kizilay, M. R. Narman, H. Song, and H. S. Narman, “Evaluating fine-tuned deep learning models for real-time earthquake damage assessment with drone-based images,” AI in Civil Engineering, vol. 3, no. 1, Sep. 2024, doi: https://doi.org/10.1007/s43503-024-00034-6.
Q. Cheng, Y. Wang, W. He, and Y. Bai, “Lightweight air-to-air unmanned aerial vehicle target detection model,” Scientific Reports, vol. 14, no. 1, p. 2609, Jan. 2024, doi: https://doi.org/10.1038/s41598-024-53181-2.
K. Madasamy, V. Shanmuganathan, V. Kandasamy, M. Y. Lee, and M. Thangadurai, “OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO,” EURASIP Journal on Image and Video Processing, vol. 2021, no. 1, May 2021, doi: https://doi.org/10.1186/s13640-021-00559-1.
L. Xu, Y. Zhao, Y. Zhai, L. Huang, and C. Ruan, “Small Object Detection in UAV Images Based on YOLOv8n,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, Aug. 2024, doi: https://doi.org/10.1007/s44196-024-00632-3.
C. Li, S. Zhou, H. Yu, T. Guo, Y. Guo, and J. Gao, “An Efficient Method for Detecting Dense and Small Objects in UAV Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 6601–6615, Jan. 2024, doi: https://doi.org/10.1109/jstars.2024.3373231.
C. Ma, Y. Fu, D. Wang, R. Guo, X. Zhao, and J. Fang, “YOLO-UAV: Object Detection Method of Unmanned Aerial Vehicle Imagery Based on Efficient Multi-Scale Feature Fusion,” IEEE Access, vol. 11, pp. 126857–126878, 2023, doi: https://doi.org/10.1109/access.2023.3329713
A. A. Laghari, A. K. Jumani, R. A. Laghari, H. Li, and S. Karim, “Unmanned Aerial Vehicles Advances in Object Detection and Communication Security Review,” Cognitive Robotics, vol. 4, pp. 128–141, Jan. 2024, doi: https://doi.org/10.1016/j.cogr.2024.07.002.
