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Journal Article
HashEye: a real-time on-drone high-resolution tiny object detection via spatial pruning
- Authors
- Hyeonji Hong, Nakyeong Lee, Kwangwoo Jang, Gague Kim, Yangjae Jeong, Chanyoung Oh
- Issue Date
- 2026-07
- Citation
- Scientific Reports, v.16, pp.1-12
- ISSN
- 2045-2322
- Publisher
- Springer Nature
- Language
- English
- Type
- Journal Article
- Abstract
- While recent deep learning-based object detection has achieved great success in various fields, it remains challenging to find tiny objects in aerial imagery on-the-fly using mobile devices. Since mobile platforms such as drones operate with limited onboard computing power, handling high-resolution images to find tiny objects with compute-intensive deep learning-based applications often fails to meet their real-time constraints. To mitigate this problem, we propose HashEye, a novel framework that enables fast on-drone tiny object detection by efficiently suppressing spatial redundancy in aerial imagery. HashEye utilizes a lightweight hashing algorithm to rapidly scan image patches; patches exhibiting high hash collision frequencies are identified as background and suppressed. Subsequently, the remaining salient patches are dynamically rearranged into a hardware-friendly dense image for efficient inference. Experimental results on two real-world datasets demonstrate that HashEye achieves up to a 5.25\(\times\) speedup compared to the baseline, maintaining detection capability.
- KSP Keywords
- Computing power, Hardware-friendly, Hashing algorithm, High-resolution Images, Image Patches, Learning-based, Mobile devices, Mobile platform, On-the-fly, Real-Time Constraints, Real-world
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
