Existing methods require paired training samples or focus primarily on visual enhancement
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
Existing methods require paired training samples or focus primarily on visual enhancement. Prior work treats perception and navigation separately, without explicitly considering their impact on downstream navigation performance. There is a need for a method that can improve the practical applicability of underwater object detection and relative navigation in real environments.
Evidence profile
Sourced from the stated challenges and stated research gap and conclusions of the source papers, classified as general, spanning 3 journals. Those papers have been cited 2 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Joint Inference of Image Enhancement and Object Detection via Cross-Domain Fusion Transformer (2026) · Computers · cited 1× · doi
Underwater degradation including low contrast, color distortion, and blur, which leads to significant challenges for underwater object detection (UOD). The introduction of artifacts or information loss during the image enhancement process, which can constrain the upper bound of detection performance. The need for computationally efficient cross-domain interaction mechanisms to achieve real-time underwater object detection.
generalstated challengesKeywords: underwater degradation including low contrast color distortion blur - Robust Underwater Object Detection and Relative Navigation Using Simulation-Aided Sonar Image Translation (2026) · Journal of Ocean Engineering and Technology · doi
Existing methods require paired training samples or focus primarily on visual enhancement. Prior work treats perception and navigation separately, without explicitly considering their impact on downstream navigation performance. There is a need for a method that can improve the practical applicability of underwater object detection and relative navigation in real environments.
generalstated research gapevidence 5/5Keywords: existing methods require paired training samples focus primarily - Advances in underwater image and video enhancement systematic review of conventional and deep learning-based method (2026) · Discover Imaging. · cited 1× · doi
In conclusion, while the current advancements in underwater image enhancement show significant promise, future work should focus on developing real-time enhance- ment models for dynamic underwater environments, integrating multi-modal data for more comprehensive improvements, and exploring hybrid models that combine deep learning with physical models to overcome challenges posed by underwater conditions.
generalconclusionsevidence 5/5Keywords: underwater models conclusion current advancements image enhancement show significant promise future focus developing real time
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