School of Computer Science and Technology, Qingdao University, China; Laboratory for Regional Oceanography and Numerical Modeling, Laoshan Laboratory, China.
Frontiers Science Center for Deep Ocean Multispheres and Earth System, School of Marine Technology, Ocean University of China, China; Laboratory for Regional Oceanography and Numerical Modeling, Laoshan Laboratory, China.
Sci Total Environ. 2023 Jun 20;878:162826. doi: 10.1016/j.scitotenv.2023.162826. Epub 2023 Mar 28.
Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain.
深海垃圾是指最终进入深海的任何持久的人为材料。海垃圾的规模和数量迅速增加，正在危及海洋的健康。因此，许多海洋社区正在努力实现一个清洁、健康、有弹性、安全和可持续收获的海洋的目标。这包括使用可操纵的水下机器清除深海垃圾。以前的研究表明，深度学习方法可以成功地从海底图像或视频中提取特征，并能够识别和检测垃圾，以方便垃圾收集。在本文中，提出了一种轻量级神经网络（称为 DSDebrisNet），可以利用检测速度和识别性能，实现高精度的即时检测。该网络旨在实现复合尺度的深海垃圾检测。在 DSDebrisNet 中，还引入了一种混合损失函数，考虑了光照和检测问题，以提高性能。此外，DSDebris 数据集是通过从 JAMSTEC 数据集提取图像和视频帧，并使用图形图像注释工具进行标记来构建的。实验是在深海垃圾数据集上进行的，结果表明，所提出的方法可以在实时环境中实现有希望的检测精度。深入研究还为人工智能向深海研究领域的成功扩展分支提供了重要证据。
