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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Low-Light Image Segmentation on Edge Computing System.

Sung-Chan Choi^1,2, Sung-Yeon Kim³

¹Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

This study presents a novel three-step algorithm for segmenting low-light images, combining brightness enhancement with U-Net segmentation. The method achieves real-time processing speeds on edge devices.

Keywords:

deep learning edge computing image segmentation low-light image

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Low-light image segmentation is difficult due to poor visibility.
Existing methods often struggle with accuracy and speed.
Tunnel crack detection requires robust low-light image analysis.

Purpose of the Study:

To develop an effective algorithm for segmenting low-light images.
To enhance image brightness and contrast before segmentation.
To achieve accurate and real-time segmentation on edge platforms.

Main Methods:

A three-step algorithm combining image enhancement and segmentation.
Utilizing deep learning for low-light image enhancement.
Employing a U-Net model for pixel-level segmentation.
Implementing the pipeline on an edge computing platform.

Main Results:

The proposed algorithm significantly improves low-light image segmentation performance.
Experimental results demonstrate superior performance compared to baseline models.
The algorithm achieves sufficient speed for real-time processing on edge devices.

Conclusions:

The combined approach of enhancement and U-Net segmentation is effective for low-light images.
The algorithm is suitable for real-time applications like tunnel crack detection.
Edge computing implementation validates the practical utility of the proposed method.