The accurate detection of small ships based on images or vision is critical for many scenarios, like maritime surveillance, port security, and navigation safety. However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirement, they have unacceptable computation costs for real-time surveillance. We propose YOLO-LPSS, a novel model designed to significantly improve small ship detection accuracy with low computation cost. The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. The experimental results show that YOLO-LPSS outperforms the known YOLOv8 nano baseline and other works, and the number of parameters increases by only 0.33 M compared to the original YOLOv8n while achieving 0.796 and 0.831 AP^50:95 in classes consisting mainly of small ship targets (the bounding box of the target area is less than 5% of the image resolution), which is 3–5% higher than the vanilla model and recent SOTA models.

In this paper, we release a new large-scale dataset containing multiple categories of ships and floating objects at sea, which we call MASS-LSVD. It is used to train and validate target detection algorithms and future large models for ship autopiloting. The dataset was captured by a visible light camera installed aboard the world’s first intelligent research, teaching, and training ship, “Xinhongzhuan”. This MASS (maritime autonomous surface ship) was operated by Dalian Maritime University, China. We have collected more than 4000 h of video of the “Xinhongzhuan” vessel’s voyage in the Bohai Sea and other areas, which are carefully classified and filtered to cover as much as possible the various types of sample data in the marine environment, such as light intensity, weather, hull shading, data from ocean-going voyages, entering and exiting ports, etc. The dataset contains 64,263 1K-resolution images captured from video footage, covering four main ship types: Fishing Boat, Bulk Carrier, Cruise Ship, Container ship, and an ‘Other Ships’ class, for vessels that cannot be specifically classified. The dataset currently contains 64,263 pairs of 1K-resolution images covering four common ship types (fishing boat, bulk carrier, cruise ship, container, and other ship, where no specific ship type can be determined). All the images have been labeled with high-precision manual bounding boxes. In this paper, the MASS-LSVD dataset is used as the basis for training various target detection algorithms and comparing them with other datasets, which compensates for the lack of first-view images in the vessel target detection dataset, and MASS-LSVD is expected to be used to facilitate the research and application of autonomous ship navigation models in the framework of computer vision.

To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual–physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual–physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxiing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual–physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin–physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations.