Robust Multi-Class Underwater Waste Detection via CNN and Transformer-Based Object Detection Models
DOI:
https://doi.org/10.15662/IJEETR.2026.0802105Keywords:
Underwater waste detection, deep learning, object detection, YOLOv8, YOLOv9, RT-DETR.Abstract
Marine pollution caused by underwater waste has become a growing environmental concern that threatens aquatic ecosystems and marine biodiversity. Detecting submerged debris in underwater environments remains a difficult task because of challenges such as low visibility, colour distortion, light absorption, and complex backgrounds. Recent advances in deep learning have made it possible to automatically detect underwater objects from visual data with improved accuracy and efficiency
This study investigates the effectiveness of deep learning–based object detection models for multi-class underwater waste detection. A dataset containing fifteen categories of underwater waste objects was obtained from the Roboflow platform. To address common underwater imaging problems, several preprocessing techniques were applied, including red channel enhancement, white balance correction, gamma correction, contrast limited adaptive histogram equalization (CLAHE), bilateral filtering, and image normalization. Three object detection models—YOLOv8, YOLOv9, and RT-DETR—were trained and evaluated using standard detection metrics such as precision, recall, and mean Average Precision (mAP).
This study investigates the effectiveness of deep learning–based object detection models for multi-class underwater waste detection. A dataset containing fifteen categories of underwater waste objects was obtained from the Roboflow platform. To address common underwater imaging problems, several preprocessing techniques were applied, including red channel enhancement, white balance correction, gamma correction, contrast limited adaptive histogram equalization (CLAHE), bilateral filtering, and image normalization. Three object detection models—YOLOv8, YOLOv9, and RT-DETR—were trained and evaluated using standard detection metrics such as precision, recall, and mean Average Precision (mAP).
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