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HashEye: a real-time on-drone high-resolution tiny object detection via spatial pruning.

Hyeonji Hong¹, Nakyeong Lee¹, Kwangwoo Jang²

¹Department of Software, Kongju National University, Chungnam, 31080, South Korea.

Scientific Reports

|May 6, 2026

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HashEye is a new framework for fast tiny object detection in aerial images on drones. It significantly speeds up processing by suppressing background areas, enabling real-time mobile applications.

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Deep learning excels at object detection but struggles with tiny objects in high-resolution aerial imagery on resource-constrained mobile devices.
Real-time detection on drones is hindered by the computational demands of processing large datasets.

Purpose of the Study:

To develop a novel framework, HashEye, for efficient and fast on-drone tiny object detection.
To address the limitations of mobile platforms in handling compute-intensive deep learning tasks for aerial imagery.

Main Methods:

HashEye employs a lightweight hashing algorithm to identify and suppress background image patches based on hash collision frequencies.
Salient image patches are then rearranged into a hardware-friendly dense format for optimized inference.

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Main Results:

HashEye achieves a speedup of up to 5.25× compared to baseline methods on real-world aerial imagery datasets.
The framework maintains its tiny object detection capabilities while significantly improving processing speed.

Conclusions:

HashEye offers an effective solution for real-time tiny object detection in aerial imagery on mobile platforms.
The proposed method successfully mitigates computational challenges, enabling efficient on-drone applications.