Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913149481910272 |
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| author | Shuvo, Sourov Roy Panth, Prajwal Chowdhury, Rajesh Chakraborty, Sorup Chakrabarty, Sudip Pattnaik, Prasant Kumar |
| author_facet | Shuvo, Sourov Roy Panth, Prajwal Chowdhury, Rajesh Chakraborty, Sorup Chakrabarty, Sudip Pattnaik, Prasant Kumar |
| contents | In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA dataset is comprised of images of different military scenarios taken from drones, and these provide a foundation for detecting military objects, but it does not take into account the various types of real-world scenarios. With that in mind, to evaluate how the models are performing under varying conditions, four different types of datasets are created: Gray Scale, Thermal Vision, Night Vision, and Obscura Vision. These simulate the real-world environments such as low visibility, heat-based imagery, and nighttime conditions. The YOLOv11-small model is trained and used to detect objects across diverse settings. This research boosts the performance and reliability of drone-based operations by contributing to the development of advanced detection systems in both defensive and offensive missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums Shuvo, Sourov Roy Panth, Prajwal Chowdhury, Rajesh Chakraborty, Sorup Chakrabarty, Sudip Pattnaik, Prasant Kumar Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA dataset is comprised of images of different military scenarios taken from drones, and these provide a foundation for detecting military objects, but it does not take into account the various types of real-world scenarios. With that in mind, to evaluate how the models are performing under varying conditions, four different types of datasets are created: Gray Scale, Thermal Vision, Night Vision, and Obscura Vision. These simulate the real-world environments such as low visibility, heat-based imagery, and nighttime conditions. The YOLOv11-small model is trained and used to detect objects across diverse settings. This research boosts the performance and reliability of drone-based operations by contributing to the development of advanced detection systems in both defensive and offensive missions. |
| title | Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2605.21157 |