The size and mass of fish are crucial parameters in aquaculture management. However, existing research primarily focuses on conducting fish size and mass estimation under ideal conditions, which limits its application in actual aquaculture scenarios with complex water quality and fluctuating lighting. A non-contact size and mass measurement framework is proposed for complex underwater environments, which integrates the improved FishKP-YOLOv11 module based on YOLOv11, stereo vision technology, and a Random Forest model. This framework fuses the detected 2D key points with binocular stereo technology to reconstruct the 3D key point coordinates. Fish size is computed based on these 3D key points, and a Random Forest model establishes a mapping relationship between size and mass. For validating the performance of the framework, a self-constructed grass carp dataset for key point detection is established. The experimental results indicate that the mean average precision (mAP) of FishKP-YOLOv11 surpasses that of diverse versions of YOLOv5–YOLOv12. The mean absolute errors (MAEs) for length and width estimations are 0.35 cm and 0.10 cm, respectively. The MAE for mass estimations is 2.7 g. Therefore, the proposed framework is well suited for application in actual breeding environments.

This study aims at developing a CFD model for large marine two-stroke dual fuel engine with gaseous fuel direct injection at high pressure. For the gaseous fuel, the shock tube theory and the pseudo-diameter concept are employed to model the injection, jet penetration and air entrainment processes, whereas its non-premixed combustion is represented by a steady diffusion flamelet model along with a pilot fuel ignition kernel. Following this model validation, a large marine two-stroke dual fuel engine closed cycle is simulated for both the gas and diesel modes at 75% load, and the involved phenomena are comparatively assessed. The derived results demonstrate that the gas mode combustion takes place in lower maximum temperature and leaner conditions compared to the diesel mode, resulting in lower NOx emissions. This study is expected to benefit the development of future engine designs and the engine settings optimisation for reducing emissions and increasing efficiency.

This study aims at the parametric investigation of the gas injection system settings of a large marine two-stroke dual fuel engine by using a developed and customized CFD method in the ANSYS Fluent software. The investigated engine injection system parameters include the gas injection timing, the gas injection duration, the gas injector lateral angle, and the gas injector holes number. Based on the comparison of the predicted performance parameters for the closed-cycle processes, the results indicate that the gas injector lateral angle is the most significant parameter that affects the engine power as well as the NO and CO₂ emissions. For satisfying the contradictory objectives of retaining the engine power and reducing the NO and CO₂ emissions, appropriate design settings for the gas injection are recommended for the investigated engine operation in the gas mode at 75% load.

Forward-looking sonar (FLS) is an important sensing modality for autonomous underwater vehicles and other marine robotic systems operating in turbid, low-visibility, and acoustically cluttered environments. Reliable target detection in FLS imagery remains challenging because target echoes are often weak, compact targets can be obscured by background clutter, and embedded processors impose strict limits on model size, latency, and computation. To address these issues, this study presents a lightweight FLS sensing framework for embedded target detection in resource-constrained underwater systems. The framework combines a compact detection architecture, difficulty-aware supervision, and teacher–student knowledge transfer. Specifically, FPN-Mix is developed as a lightweight backbone with a Conv-Mix module to improve contextual aggregation under limited computational budgets. A target-aware dynamic weighting loss is introduced to increase the supervision weight of difficult acoustic samples associated with weak echoes, ambiguous boundaries, and clutter interference. A multi-level knowledge distillation strategy is then adopted to transfer feature-level and prediction-level knowledge from an enhanced teacher model to the compact student detector. Experiments on the public UATD benchmark and the independently collected Zhanjiang Bay No.1 field dataset show that the proposed method achieves a favorable balance between detection accuracy and efficiency and remains competitive in a real marine aquaculture environment. The proposed model contains only 2.83 M parameters and requires 6.68 GFLOPs. After ONNX export and TensorRT FP16 acceleration, the model reaches 72.23 frames per second (FPS) on an NVIDIA Jetson Orin NX platform, supporting its practical use in embedded FLS sensing systems.

Accurate monitoring of fish movement is essential for understanding behavioral patterns and group dynamics in aquaculture systems. Underwater scenes—characterized by dense populations, frequent occlusions, non-rigid body motion, and visually similar appearances—present substantial challenges for conventional multi-object tracking methods. We propose an improved CenterTrack-based framework tailored for multi-fish tracking in such environments. The framework integrates three complementary components: a multi-branch feature extractor that enhances discrimination among visually similar individuals, occlusion-aware output heads that estimate visibility states, and a three-stage cascade association module that improves trajectory continuity under abrupt motion and occlusions. To support systematic evaluation, we introduce a self-built dataset named Multi-Fish 25 (MF25), continuous video sequences of 75 individually annotated fish recorded in aquaculture tanks. The experimental results on MF25 show that the proposed method achieves an IDF1 of 82.5%, MOTA of 85.8%, and IDP of 84.7%. Although this study focuses on tracking performance rather than biological analysis, the produced high-quality trajectories form a solid basis for subsequent behavioral studies. The framework’s modular design and computational efficiency make it suitable for practical, online tracking in aquaculture scenarios.