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Date of Award
2026
Document Type
Campus Access Dissertation
Degree Name
Doctor of Philosophy in Computational Science and Engineering (PhD)
Administrative Home Department
Department of Applied Computing
Advisor 1
Ashraf Saleem
Committee Member 1
Evan Lucas
Committee Member 2
Sidike Paheding
Committee Member 3
Nathir A. Rawashdeh
Committee Member 4
Jung Yun Bae
Abstract
Underwater images suffer from color distortion, low contrast, and reduced visibility due to light absorption and scattering in water. Image enhancement methods aim to correct these degradations, but their effect on downstream tasks such as object detection is poorly understood. This dissertation investigates the relationship between underwater image enhancement and object detection through six interconnected studies presented across the introduction, three core chapters, a consolidated Applications chapter, and conclusions. Two datasets are introduced: CUPDD, a unique underwater plant detection dataset from the Great Lakes region, and RUOD-R, a professional re-annotation of the widely used RUOD benchmark that contains 3.5 times more object instances and over 140 times more small objects than the original. A systematic evaluation of nine enhancement methods shows that quality metrics do not predict detection performance. A unified evaluation framework with per-image analysis reveals that approximately 80% of enhanced images match or outperform originals in detection, while a small fraction of over-enhanced images drives the overall performance drop. The investigation also uncovers that label incompleteness in existing benchmarks causes many valid detections to be counted as false positives, and a flip rate analysis on RUOD finds that 38.1% of detections counted as false positives on the original annotations are valid detections of real objects. Enhancement can also support human labeling and annotation when degraded frames are hard to interpret. Applied work on plant detection, mixed-domain training with enhancement as augmentation, and synthesis of underexposed imagery is summarized in one Applications chapter. The first applied study shows that object detection performance is relatively poor on visually degraded underwater images in CUPDD. In the round goby study, using enhancement as an augmentation during detector training improves detection without changing the detector at inference. The complementary idea of deliberate image degradation, through underexposure synthesis, can help by augmenting training with labeled synthetically underexposed images when real dark scene examples are scarce. A perceptual optimization approach (PDFO) yields synthetic underexposure closer to real low light data than simpler histogram based synthesis.
Recommended Citation
Awad, Ali, "Bridging Image Enhancement and Object Detection in Underwater Environments: Datasets, Evaluation Frameworks, and Training Strategies", Campus Access Dissertation, Michigan Technological University, 2026.