AquaClean AI: Intelligent Underwater Trash Detection using Hybrid CNN and YOLO-based Deep Learning for Marine Pollution Monitoring
Keywords:
Artificial intelligence, Computer vision, Deep learning, Efficient net, Environmental monitoring, Hybrid CNN, Marine pollution, Underwater trash detection, VGG16, YOLOAbstract
Marine pollution is emerging as a critical global issue that threatens aquatic ecosystems and biodiversity at multiple levels. The accumulation of underwater waste disrupts ecological balance and demands intelligent monitoring solutions. Within the paradigm of deep learning-driven environmental systems, AquaClean AI is conceptualized as an intelligent framework that enables automated detection of underwater trash. The system integrates hybrid convolutional neural networks combining VGG16 and EfficientNet to extract deep visual representations from complex underwater imagery. It further employs YOLO-based object detection to localize and classify debris under challenging environmental conditions. The framework operationalizes computer vision principles to process distorted images affected by low visibility and noise while ensuring reliable detection performance. By synthesizing feature extraction and real-time detection into a unified pipeline, the system transforms raw underwater data into meaningful insights. The proposed approach demonstrates strong adaptability across diverse underwater scenarios and supports continuous monitoring applications. Experimental evaluation demonstrates high detection accuracy, with YOLO-based models achieving mAP of 0.96, precision of 0.94-0.96, and recall of 0.91-0.93, outperforming traditional detection approaches. The solution offers a scalable, intelligent approach to addressing underwater pollution using advanced artificial intelligence techniques.
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