Deep-Sea Debris Identification Using Deep Convolutional Neural Networks

Deep-sea debris is a globally growing problem that negatively impacting biological and chemical ecosystems. More seriously, the debris is likely to persist in the deep sea for long periods. Fortunately, with the help of the debris detection system the submersibles can clean up the debris. An excellent classifier is critical to the debris detection system. Therefore the objective of this study is to determine whether deep convolutional neural networks can distinguish the differences of debris and natural deep-sea environment, so as to effectively achieve deep-sea debris identification. First, a real deep-sea debris images (DDI) dataset is constructed for further classification research based on an online deep-sea debris database owned by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Second, the hybrid Shuffle-Xception network is constructed to classify the deep-sea image as metal, glass, plastic, rubber, fishing net \&rope, natural debris, and cloth. Furthermore, five common convolutional neural networks (CNNs) frameworks are also employed to implement the classification process. Finally, the identification experiments are carried out to validate the performance of the proposed methodology. The results demonstrate that the proposed method is superior to the state-of-art CNN methods and has the potential for deep-sea debris identification.