Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection

2024-11-21

AI Summary

Underwater image degradation severely impairs object detection performance. This work systematically evaluates the impact of nine state-of-the-art underwater image enhancement methods—including physics-based, non-physics-based, and deep learning approaches—on modern detectors such as YOLOv8 and RT-DETR, correlating enhancement outcomes with both UIQM/UCIQE quality metrics and detection mAP. We uncover, for the first time, a dual paradox: while enhancement consistently degrades average detection accuracy across datasets (negative correlation with mAP), it significantly improves detection on specific individual images (positive correlation at the per-image level). Motivated by this finding, we propose a novel “image-wise adaptive enhancement” paradigm, identifying a subset of enhancement methods that reliably boost per-image detection performance. Extensive experiments confirm that enhancement yields no universal gain; instead, its benefit is highly image-dependent. To foster reproducibility and further research, we publicly release all source code, enhanced images, detection outputs, and analytical tools.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Underwater imagery often suffers from severe degradation resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://github.com/RSSL-MTU/Enhancement-Detection-Analysis.

Problem

• Evaluating image enhancement models for underwater object detection
• Analyzing enhancement effects on detection performance metrics
• Exploring selective enhancement to improve individual image detection

Innovation

• Evaluates nine underwater image enhancement models
• Analyzes enhancement effects on object detection
• Correlates enhancement metrics with detection accuracy