The rapid accumulation of marine debris poses a substantial threat to ocean ecosystems, specifically the degradation of plastics, composite materials, and metals. Effective detection and classification of debris by material type are essential during the waste management and material recycling process. However, the underwater debris detection is often hampered by low contrast, color distortion, and noise in underwater imagery. Different deep learning models are proposed in the literature for debris detection, however most of them suffered with limitations in real time implementation complexity and inaccurate instance segmentation. This work proposes an adaptive hybrid lightweight Mask R-CNN system including image augmentation, object identification, and real-time instance segmentation to handle these issues. The preprocessed images are feed to the lightweight mask RCNN model for the object detection and segmentation. The proposed model uses an upgraded Region Proposal Network (RPN) for exact localization of underwater trash and MobileNetV3 as a lightweight backbone for effective feature representation. The model uses data augmentation methods including contrast correction, flipping, and blurring to improve robustness; the model also trained on the proprietary underwater debris datasets. Compared to the conventional approaches, performance evaluation metrics employing Mean Average Precision (mAP), Structural Similarity Index Measure (SSIM), Intersection over Union (IoU), and Peak Signal to Noise Ratio (PSNR) shows better accuracy. Furthermore, the model performs real-time computing at 30 FPS, which makes it highly suitable for usage in real-time operations.
Deluxni et al. (2026) studied this question.