Abstract
Deep learning techniques have been explored within the marine litter problem for approximately 20 years but the majority of the research has developed rapidly in the last five years. We provide an in-depth, up to date, summary and analysis of 28 of the most recent and significant contributions of deep learning in marine debris. From cross referencing the research paper results, the YOLO family significantly outperforms all other methods of object detection but there are many respected contributions to this field that have categorically agreed that a comprehensive database of underwater debris is not currently available for machine learning. Using a small dataset curated and labelled by us, we tested YOLOv5 on a binary classification task and found the accuracy was low and the rate of false positives was high; highlighting the importance of a comprehensive database. We conclude this survey with over 40 future research recommendations and open challenges.
Abstract (translated)
深度学习技术在海洋垃圾问题中进行了约20年的探索，但近五年来，大部分研究都发展迅速。我们提供了对28个最近和最重要的海洋垃圾领域深度学习最新贡献的深入、最新和详细的总结和分析。通过引用研究论文的结果，YOLO家族在所有其他物体检测方法中显著优于其他方法，但在这个领域，有很多值得尊重的贡献，他们 categorically 同意目前还没有为机器学习提供一个全面的海洋垃圾数据库。使用我们精心策划和标签的小数据集，我们对YOLOv5在二分类任务上的表现进行了测试，发现准确率较低，假阳性率较高，突出了全面数据库的重要性。我们结束这次调查，提出了超过40个未来的研究建议和开放性挑战。
URL
https://arxiv.org/abs/2403.18067
