EEG-Based Maritime Object Detection for IoT-Driven Surveillance Systems in Smart Ocean

Abstract

Automated maritime object detection is a significant research challenge in intelligent marine surveillance systems for the Internet of Things (IoT) and smart ocean applications. In particular, ship detection is recognized as one of the core research issues of these IoT-driven intelligent marine surveillance systems. Traditional methods based on machine learning have made some achievements in detection tasks for specific objects. However, the ship objects are relatively small, and they are usually not accurately detected. In this article, we propose an electroencephalography (EEG)-based maritime object detection algorithm for IoT-driven surveillance systems in the smart ocean. For this purpose, we conduct experiments to record the EEG signals of subjects when they are watching the maritime image scenes. With the feature analysis of EEG signals, the event-related potential (ERP) components associated with detecting objects are induced, such as the $P3$ and $N2$ components. Employing classification based on linear discriminant analysis (LDA), the area under curve (AUC) of the receiver operating characteristic (ROC) is used to evaluate the detection accuracy. We use this novel method to determine and identify essential objects and areas from IoT devices, such as digital camera imaging sensors. Our proposed method can not only help to detect small objects accurately using fewer samples but can also be used to reduce the data volume needed to be stored and transmitted in IoT-driven marine surveillance systems.