Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand

Abstract

Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, this field study conducted in Koh Tao, Thailand, proposed an innovative and cost-effective approach that leverages super-resolution reconstruction (SRR) technology in conjunction with an optimized object detection model based on YOLOv8. Super-resolution (SR) images reconstructed by seven SRR models were fed into the proposed Seafloor-Debris-YOLO (SFD-YOLO) model for seafloor debris object detection. RDN model achieved the highest reconstruction results with a signal-to-noise ratio (PSNR) of 41.02 dB and structural similarity (SSIM) of 95.08 % and attained state-of-the-art (SOTA) accuracy in debris detection with a mean Average Precision (mAP) of 91.2 % using RDN-reconstructed images with a magnification factor of 4. Additionally, this study provided an in-depth analysis of the influence of magnification factors within the SRR process, offering a quantitative comparison of images before and after reconstruction, as well as a comparative evaluation across various detection algorithms with a novel pretraining strategy. This approach to underwater survey methods, combined with SRR technology, marks an advancement in the field of seafloor debris monitoring, presenting practical solutions to enhance image quality affected by field conditions and enabling the precise identification of marine debris.