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Related Experiment Video

Updated: Jan 28, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Revisiting Underwater Image Enhancement for Object Detection: A Unified Quality-Detection Evaluation Framework.

Ali Awad¹, Ashraf Saleem¹, Sidike Paheding²

¹Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USA.

Journal of Imaging

|January 27, 2026

Summary

Underwater image enhancement can improve object detection, but only for low-quality images. Selective enhancement offers significant gains over no or full enhancement, guiding future computer vision task optimization.

Keywords:

enhancement–detection interaction image enhancement image quality assessment mAP analysis object detection per-image evaluation underwater imaging

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Underwater images suffer from poor quality (color distortion, low contrast, reduced visibility).
Image enhancement is commonly used, but its benefit for object detection is debated.
Existing evaluations lack rigor and granularity.

Purpose of the Study:

To comprehensively evaluate state-of-the-art underwater image enhancement methods for object detection.
To propose a unified framework for rigorous, fine-grained performance assessment.
To investigate the impact of enhancement on detection accuracy at both dataset and image levels.

Main Methods:

Developed a unified evaluation framework integrating quality assessment (Q-index) and per-image detection (COCO mAP).
Evaluated nine enhancement methods against modern object detectors.
Conducted mixed-set analysis to determine theoretical performance limits of selective enhancement.

Main Results:

Traditional quality metrics do not reliably predict object detection performance.
Dataset-level analysis obscures significant image-level variability.
Enhancement improves detection accuracy for low-quality inputs but can degrade performance in other cases.
Selective enhancement yields substantial gains over original and fully enhanced datasets.

Conclusions:

Underwater image enhancement can benefit object detection when evaluated granularly.
Selective enhancement strategies are crucial for optimizing performance in computer vision tasks.
This study provides evidence for the judicious application of enhancement techniques.