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

Benrong Huang,

Jiaqi Wang

et al.

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 214, P. 117710 - 117710

Published: Feb. 20, 2025

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

Language: Английский

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

Benrong Huang,

Jiaqi Wang

et al.

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 214, P. 117710 - 117710

Published: Feb. 20, 2025

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

Language: Английский

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