Underwater Simultaneous Enhancement and Super-Resolution Impact Evaluation on Object Detection
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
Underwater imagery often exhibits significant degradation and poor quality as compared to outdoor imagery. To compensate for this, Single-Image Super-Resolution (SISR) and enhancement algorithms are used to lessen this degradation and produce high-resolution images. In this study, we apply state-of-the-art Simultaneous Enhancement and Super-Resolution (SESR) and SISR models to different sets of downscaled images from the comprehensive RUOD dataset. We then conduct a qualitative and quantitative analysis of the upscaled and enhanced images using standard underwater image quality metrics (IQMs). Subsequently, we evaluate the robustness of the state-of-the-art YOLO-NAS detector against image sets with varying downscaled spatial resolutions. Lastly, we examine the impact that the SISR and SESR models has on YOLO-NAS detector performance. The findings reveal a decline in the detection performance on the downscaled test images and a further decline on the upscaled and enhanced images produced by SISR and SESR models, suggesting a negative relationship between such models and detection.
Publication Title
Proceedings of SPIE - The International Society for Optical Engineering
ISBN
[9781510673984]
Recommended Citation
Awad, A.,
Zahan, N.,
Lucas, E.,
Havens, T.,
Paheding, S.,
&
Saleem, A.
(2024).
Underwater Simultaneous Enhancement and Super-Resolution Impact Evaluation on Object Detection.
Proceedings of SPIE - The International Society for Optical Engineering,
13040.
http://doi.org/10.1117/12.3014034
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/961