Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Ali Awad; Nusrat Zahan; Evan Lucas; Timothy C. Havens; Sidike Paheding; Ashraf Saleem

doi:10.1117/12.3014034

7 June 2024 Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Ali Awad, Nusrat Zahan, Evan Lucas, Timothy C. Havens, Sidike Paheding, Ashraf Saleem

Proceedings Volume 13040, Pattern Recognition and Prediction XXXV; 1304009 (2024) https://doi.org/10.1117/12.3014034
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ali Awad, Nusrat Zahan, Evan Lucas, Timothy C. Havens, Sidike Paheding, and Ashraf Saleem "Underwater simultaneous enhancement and super-resolution impact evaluation on object detection", Proc. SPIE 13040, Pattern Recognition and Prediction XXXV, 1304009 (7 June 2024); https://doi.org/10.1117/12.3014034

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