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Recognition Task-Based Detection Score: A Task-Oriented Evaluation Metric for Infrared Image Colorization.

Hao Wang^1,2, Jiaming Cai^1,2, Yao Hu^1,2

¹School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

A new metric, Recognition-Task based Detection Score (RDS), evaluates infrared image colorization by measuring object detection accuracy. This task-oriented approach ensures colorization quality aligns with practical usability in downstream applications.

Keywords:

infrared image colorization object detection quality evaluation task-oriented metric

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Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS

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Published on: May 3, 2011

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Infrared image colorization enhances visibility but current evaluation metrics lack practical task relevance.
Existing methods focus on pixel-level or feature-level differences, not usability in real-world applications.

Purpose of the Study:

To introduce a novel task-oriented evaluation metric for infrared image colorization.
To develop a metric that aligns image quality assessment with performance in downstream tasks.

Main Methods:

Proposed the Recognition-Task based Detection Score (RDS) using object detection accuracy on colorized images.
Incorporated position robustness, fine-grained interpretability (category-level accuracy), and task adjustability.
Validated RDS on NIR-RGB and FLIR-5C datasets.

Main Results:

RDS shows good consistency with traditional metrics under standard conditions.
RDS demonstrates superior stability and robustness against registration errors compared to PSNR.
RDS provides fine-grained interpretability and task adjustability, outperforming traditional metrics.

Conclusions:

RDS offers a more practical and reliable criterion for evaluating infrared colorization models.
The metric's task-oriented nature improves the assessment of colorization usability.
RDS facilitates better optimization of infrared colorization techniques for real-world applications.