Underwater Simultaneous Enhancement and Super-Resolution Impact Evaluation on Object Detection

Document Type

Conference Proceeding

Publication Date

6-7-2024

Department

College of Computing; Michigan Tech Research Institute; Department of Applied Computing

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]

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