Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

Abstract

Vessel monitoring technology involves the application of remote sensing technologies to detect and identify vessels in various environments, which is critical for monitoring vessel traffic, identifying potential threats, and facilitating maritime safety and security to achieve real-time maritime awareness in military and civilian domains. However, most existing vessel monitoring models tend to focus on a single remote sensing information source, leading to limited detection functionality and underutilization of available information. In light of these limitations, this paper proposes a comprehensive ship monitoring system that integrates remote satellite devices and nearshore detection equipment. The system employs ResNet, a deep learning model, along with data augmentation and transfer learning techniques to enable bidirectional detection of satellite cloud images and nearshore outboard profile images, thereby alleviating prevailing issues such as low detection accuracy, homogeneous functionality, and poor image recognition applicability. Empirical findings based on two real-world vessel monitoring datasets demonstrate that the proposed system consistently performs best in both nearshore identification and remote detection. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of different modules and discuss the constraints of current deep learning-based vessel monitoring models.

LFLD-CLbased NET: A Curriculum-Learning-Based Deep Learning Network with Leap-Forward-Learning-Decay for Ship Detection

“…Zhang et al [23] used the feature of DCT blocks to extract horizon information for efficient ship detection. Li et al [3] proposed a bidirectional ship monitoring system that utilizes satellite devices and near-shore surveillance cameras based on knowledge transfer.…”

“…Ship detection, as one of the most important intelligent maritime perception technologies, has recently attracted significant interest from researchers and scholars in ocean surveillance [1][2][3]. With the development of artificial intelligence, deep-learning-based ship detection methods are now dominating the maritime domain awareness (MDA) field due to their remarkable data fitting ability [4,5], which has achieved a breakthrough in the accuracy of ship identification compared to the traditional methods.…”

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

“…Dynamic monitoring of fishing vessel behavior can better ensure the safety of fishing vessels and reduce response times for rescue at sea. Although the existing traditional ship monitoring systems are equipped with intelligent AIS, VMS, global positioning systems (GPS) and other radio communication equipment, they lack real-time analysis and processing of data, cannot cope with the situation of multiple vessels entering and leaving the port at the same time, cannot provide efficient monitoring, early warning and management of "three-no fishing vessels", and their limitations make IUU behavior evade detection [25]. Therefore, it is necessary to improve the dynamic monitoring of fishing vessels at sea with the help of advanced AI technology.…”

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

“…While the range of coastal radar is restricted compared to satellite systems, it is a traditional component in vessel traffic service systems for monitoring non-cooperating vessels in highly trafficked areas [52]. There are many examples of coastal radar as a component of a common operating picture that considers multiple inputs (e.g., [53][54][55]). Persistent target tracking, facilitated by the Automatic Radar Plotting Aid (ARPA), provides a continuous record of a unique vessel's trajectory, which is difficult to reconstruct from infrequent satellite images [56].…”