Maritime equipment group gaming (MEGG) provides a critical framework for analyzing strategy interactions among groups of maritime equipment engaged in complex maritime operations, such as adversarial simulation, maritime traffic, and maritime rescue. The equipment groups typically include manned and unmanned vessels, carrier-based aircraft, and other similar assets, and the strategy interactions examined in MEGG primarily involve confrontation, competition, and cooperation. First, the survey clarifies the concept of maritime equipment group gaming by distinguishing it from related paradigms such as population games and swarm intelligence. This conceptual clarification helps lay the groundwork for subsequent classification and analysis. Next, the survey reviews typical MEGG task scenarios, including adversarial simulation, maritime traffic, and maritime rescue. Each of these scenarios presents distinct operational challenges and objectives within the MEGG framework. The survey then classifies the basic types of MEGG approaches. Specifically, MEGG approaches are categorized along multiple dimensions: game modes confrontation, competition, cooperation, and mixed), game scope (intra-group, inter-group, and dual-level), equipment heterogeneity (single-class and cross-class systems), and intelligence levels (from non-intelligent to intelligent systems). Second, the survey reviews technological progress in two main categories: non-intelligent and intelligent MEGGs. For non-intelligent MEGG, classical methods such as Lanchester's laws, population game theory, and crowd simulation models are reviewed. For intelligent MEGG, the survey reviews the MEGG approaches based on traditional machine learning (including decision support, scheduling, planning, simulation, prediction) and multi-agent reinforcement learning (focusing on adversarial simulation, task planning, and so on). Finally, the survey summarizes current challenges in MEGG research and proposes six promising directions: a human-machine integrated intelligent decision-making framework for gaming, trustworthiness and interpretability of intelligent gaming models, deep reasoning for maritime missions using large-scale models, hierarchical collaborative gaming mechanisms for intelligent agents; a standardized management system for heterogeneous intelligent agent clusters, and high-fidelity gaming systems enhanced by cross-domain expert knowledge. These directions collectively provide structured and actionable guidance for future research in this emerging and significant field.

Knowledge distillation is often used for model compression and has achieved a great breakthrough in image classification, but there still remains scope for improvement in object detection, especially for knowledge extraction of small objects. The main problem is the features of small objects are often polluted by background noise and not prominent due to down-sampling of convolutional neural network (CNN), resulting in the insufficient refinement of small object features during distillation. In this paper, we propose Hierarchical Matching Knowledge Distillation Network (HMKD) that operates on the pyramid level P2 to pyramid level P4 of the feature pyramid network (FPN), aiming to intervene on small object features before affecting. We employ an encoder-decoder network to encapsulate low-resolution, highly semantic information, akin to eliciting insights from profound strata within a teacher network, and then match the encapsulated information with high-resolution feature values of small objects from shallow layers as the key. During this period, we use an attention mechanism to measure the relevance of the inquiry to the feature values. Also in the process of decoding, knowledge is distilled to the student. In addition, we introduce a supplementary distillation module to mitigate the effects of background noise. Experiments show that our method achieves excellent improvements for both one-stage and two-stage object detectors. Specifically, applying the proposed method on Faster R-CNN achieves 41.7% mAP on COCO2017 (ResNet50 as the backbone), which is 3.8% higher than that of the baseline.

Trajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Current works typically treat pedestrian trajectories as a series of 2D point coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observation and other movement characteristics of pedestrians, we propose a simple and intuitive movement description called a trajectory distribution, which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribution in space. Based on this novel description, we develop a new trajectory prediction method, which we call the social probability method. The method combines trajectory distributions and powerful convolutional recurrent neural networks. Both the input and output of our method are trajectory distributions, which provide the recurrent neural network with sufficient spatial and random information about moving pedestrians. Furthermore, the social probability method extracts spatio-temporal features directly from the new movement description to generate robust and accurate predictions. Experiments on public benchmark datasets show the effectiveness of the proposed method.