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

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.