As a mainstream framework in the field of semi-supervised learning (SSL), self-training via pseudo labeling and its variants have witnessed impressive progress in semi-supervised semantic segmentation with the recent advance of deep neural networks. However, modern self-training based SSL algorithms use a pre-defined constant threshold to select unlabeled pixel samples that contribute to the training, thus failing to be compatible with different learning difficulties of variant categories and different learning status of the model. To address these issues, we propose Enhanced Soft Label (ESL), a curriculum learning approach to fully leverage the high-value supervisory signals implicit in the untrustworthy pseudo label. ESL believes that pixels with unconfident predictions can be pretty sure about their belonging to a subset of dominant classes though being arduous to determine the exact one. It thus contains a Dynamic Soft Label (DSL) module to dynamically maintain the high probability classes, keeping the label "soft" so as to make full use of the high entropy prediction. However, the DSL itself will inevitably introduce ambiguity between dominant classes, thus blurring the classification boundary. Therefore, we further propose a pixel-to-part contrastive learning method cooperated with an unsupervised object part grouping mechanism to improve its ability to distinguish between different classes. Extensive experimental results on Pascal VOC 2012 and Cityscapes show that our approach achieves remarkable improvements over existing state-of-the-art approaches.

Accurate prediction of ship trajectories is crucial for ensuring safe and efficient navigation. However, predicting ship trajectories in complex and dynamic environments presents significant challenges. Ships exhibit multi-mode motions, manifesting as diverse motion patterns even under similar circumstances, influenced by factors such as navigational intentions and operational tasks. Moreover, trajectory prediction is further complicated by time-varying ship dynamics, encompassing sailing conditions, ship maneuvering, and environmental factors. In this paper, we propose a Bayesian multiple model with an online model selection strategy to dynamically represent the latent motion mode from early observations. Each sub-model integrates a variational Kalman filter and Gated Recurrent Unit (GRU) neural network, enabling the estimation of time-varying transition coefficients and the process noise specific to different motion modalities. This hybrid methodology leverages the strengths of probabilistic recursive estimation of the Kalman filter while benefiting from the capacity of a GRU network to learn complex temporal dependencies from historical data. The proposed method was evaluated on ship trajectories across different observation lengths and prediction horizons and outperformed the baseline in terms of both accuracy and plausibility.

Device-free indoor localization may play a critical role in improving passengers’ safety in large vessels, particularly for scenarios without equipped radios. However, due to dynamic internal and external influences from the sailing ship such as changing sailing speed, existing localization systems suffer huge accuracy degradation in a mobile ship environment. The challenges are mainly due to rich and arbitrary ship motions and the resulting complicated impacts on the indoor wireless channels. To address the challenges, in this paper, we first propose a ship motion descriptor to extract discriminative latent representation from complex ship motions by leveraging deep-learning techniques. Based on this representation, we then design a novel fingerprint roaming model, i.e., MoLoc, to automatically learn the predictive fingerprint variation pattern and transfer the online fingerprint measurement to adapt to dynamic ship motions in real-time. Furthermore, an unsupervised learning strategy is proposed to train the fingerprint roaming model using unlabelled onboard collected data which does not incur any labor costs. We have implemented and extensively evaluated MoLoc on real-world cruise ships, where experimental results demonstrate that MoLoc improves localization accuracy from 63.2% to 92.8% compared to the state-of-the-art localization methods including Pilot, LiFS, SpotFi, and AutoFi, while achieving a mean error of 0.68m.

Ship collision accidents frequently cause casualties and significant property losses. These collisions mainly occur by incorrectly interpreting the intents of other ships’ navigators. However, inferring the avoidance intents of other ships is challenging, because the uncertain motions and the long-lasting dynamic interactions during encounters usually obscure the true intents. To address this, we propose a probabilistic graph model to infer the hidden avoidance intent of ships. Specifically, the dynamic encounter evolution is expressed in a model represented by a factor graph, where the intent beliefs are accumulated and propagated over time through message passing in the graph. In the inference procedure, we develop a hybrid Bayesian inference approach, integrating a data-driven component derived from empirical priors fitted to historical data, and a model-driven component capturing the ship’s control process. We derive 9 types of intents from naturalistic ship encounters and evaluate the model on a validation split. The quantitative metrics demonstrate that, on average, the proposed procedure can accurately infer the intents 14.04 seconds in advance and outperforms the baseline in macro-averaged recall rate (0.2919) and F1-score (0.2843). Note to Practitioners —This work aims to enhance safety in ship navigation by introducing an intelligent navigation support system to prevent collisions. A significant cause of ship collision accidents is the misinterpretation of other navigators’ intents. Estimating the intents of marine traffic participants can significantly reduce collision rates. However, real-time intent inference in ship encounters poses challenges due to complex dynamics and uncertain motions. We propose a novel probabilistic graphical model that allows us to infer avoidance intents, evaluated on a naturalistic Automatic Identification System (AIS) dataset, demonstrating effectiveness in real-world scenarios. The practical implications of our work are substantial for the maritime industry. Ship captains and intelligent automated vessels can use our system to enhance situational awareness, enabling informed collision avoidance decisions. Our work aims to improve the safety and efficiency of ship navigation, reducing collision risks and ensuring the well-being of crew members and valuable cargo.

Efficient and safe ship evacuation strategy plays a critical role in protecting passengers’ lives when ships encounter accidents. Existing wireless sensor network (WSN)-based emergency navigation methods mainly consider the dynamics of hazards and accordingly plan evacuation paths to minimize human exposure to the environmental hazards, such as fire and smoke. However, without sufficient consideration of the ship capsizing time and the impact of dynamic ship inclination on the passengers’ walking speed, these methods may fail to evacuate passengers before the deadline. In this paper, we propose ANT, a deadlineaware adaptive emergency navigation strategy for dynamic hazardous ship evacuation with WSNs, which informs each passenger about a hazard-avoided evacuation path to successfully reach lifeboats within the specified deadline under all circumstances. To achieve this aim, ANT analyzes the process of ship capsizing to predict the specific limited evacuation time and the worst-case traversal delay. Next, an online evacuation path planning strategy is proposed based on a real-time adaptive routing algorithm to maximize navigation efficiency while ensuring user safety.We evaluate ANT by conducting prototype experiments and extensive simulations. The results demonstrate that ANT improves the navigation success ratio by 40% and 5%, compared with state-of-the-art emergency navigation systems, namely, medial axis-based approach and ENO-based oscillation-free emergency navigation approach, respectively.