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.

Ship detection in the maritime domain awareness field has seen a significant shift towards deep-learning-based techniques as the mainstream approach. However, most existing deep-learning-based ship detection models adopt a random sampling strategy for training data, neglecting the complexity differences among samples and the learning progress of the model, which hinders training efficiency, robustness, and generalization ability. To address this issue, we propose a ship detection model called the Leap-Forward-Learning-Decay and Curriculum Learning-based Network (LFLD-CLbased NET). This model incorporates innovative strategies as Leap-Forward-Learning-Decay and curriculum learning to enhance its ship detection capabilities. The LFLD-CLbased NET is composed of ResNet as the feature extraction unit, combined with a difficulty generator and a difficulty scheduler. The difficulty generator in LFLD-CLbased NET effectively expands data samples based on real ocean scenarios, and the difficulty scheduler constructs corresponding curriculum training data, enabling the model to be trained in an orderly manner from easy to difficult. The Leap-Forward-Learning-Decay strategy, which allows for flexible adjustment of the learning rate during curriculum training, is proposed for enhancing training efficiency. Our experimental findings demonstrate that our model achieved a detection accuracy of 86.635%, approximately 10% higher than other deep-learning-based ship detection models. In addition, we conducted extensive supplementary experiments to evaluate the effectiveness of the learning rate adjustment strategy and curriculum training in ship detection tasks. Furthermore, we conducted exploratory experiments on different modules to compare performance differences under varying parameter configurations.

Automatic ship monitoring models leveraging image recognition have become integral to regulatory applications within maritime management, with multi-source image co-monitoring serving as the primary method for achieving comprehensive, round-the-clock surveillance. Despite their widespread use, the existing models predominantly train each data source independently or simultaneously train multiple sources without fully optimizing the integration of similar information. This approach, while capable of all-weather detection, results in the underutilization of data features from related sources and unnecessary repetition in model training, leading to excessive time consumption. To address these inefficiencies, this paper introduces a novel multi-task learning framework designed to enhance the utilization of data features from diverse information sources, thereby reducing training time, lowering costs, and improving recognition accuracy. The proposed model, VIOS-Net, integrates the advantages of both visible and infrared data sources to meet the challenges of all-weather, all-day ship monitoring under complex environmental conditions. VIOS-Net employs a Shared Bottom network architecture, utilizing both shared and specific feature extraction modules at the model’s lower and upper layers, respectively, to optimize the system’s recognition capabilities and maximize data utilization efficiency. The experimental results demonstrate that VIOS-Net achieves an accuracy of 96.20% across both visible and infrared spectral datasets, significantly outperforming the baseline ResNet-34 model, which attained accuracies of only 4.86% and 9.04% in visible and infrared data, respectively. Moreover, VIOS-Net reduces the number of parameters by 48.82% compared to the baseline, achieving optimal performance in multi-spectral ship monitoring. Extensive ablation studies further validate the effectiveness of the individual modules within the proposed framework.

Deep learning-based intelligent ship surveillance technology has become an indispensable component of modern maritime intelligent perception, with its adversarial defense capabilities serving as a crucial guarantee for reliable and stable monitoring. However, current research on deep learning-based ship surveillance primarily focuses on minimizing the discrepancy between predicted labels and ground truth labels, overlooking the equal importance of enhancing defense capabilities in the adversarial technology-laden maritime environment. To address this challenge and improve model robustness and stability, this study proposes a novel framework termed the Robust Adversarial Fusion Surveillance Net Framework (RAFS-Net). Utilizing ResNet as the backbone network foundation, the framework constructs a ship adversarial attack chain through an adversarial generation module. An adversarial training module enables the model to comprehensively learn adversarial perturbation features. These dual modules effectively rectify abnormal decision boundaries via a synergistic mechanism, compelling the model to learn robust feature representations resilient to malicious interference. Experimental results demonstrate that the framework maintains stable and efficient detection capabilities even in marine environments saturated with interfering information. By systematically integrating gradient-driven adversarial sample generation and an end-to-end training mechanism, it achieves a performance breakthrough of 9.1% in mean Average Precision (mAP) on the ship adversarial benchmark dataset, providing technical support for maritime surveillance models in complex adversarial environments.