The escalating marine plastic pollution crisis requires automated, scalable solutions for monitoring. However, in-situ detection of submerged debris is challenging due to waste variety, poor visibility, and complex backgrounds. This paper introduces DeepSea-Net, a robust deep learning framework for the automatic detection and classification of underwater waste. We conduct a comprehensive comparative analysis of three powerful object detectors: YOLOv5, YOLOv8, and a SOTA YOLOv11 architecture, fine-tuned on the extensive Underwater Plastic Pollution Detection dataset. Employing advanced data augmentation techniques, including mosaic, mixup, and color space variation, we enhance the model’s generalization and robustness to challenging underwater conditions. Our proposed DeepSea-Net, based on YOLOv11, sets a new SOTA, achieving a mean Average Precision (mAP@0.5) of 79.53%. The framework demonstrates a high recall and a leading F1-Score of 74.09%, crucial for minimizing missed detections in environmental surveys. Achieving an inference latency of 16.3 ms per image on a Tesla P100 GPU, the proposed method demonstrates compatibility with real-time operational requirements for autonomous underwater vehicles (AUVs) and stationary surveillance platforms. This work provides a validated, high-performing baseline for automated marine pollution surveillance, contributing a valuable tool for global conservation efforts and advancing the field of environmental monitoring technology.