Tiny person detection based on computer vision technology is critical for maritime emergency rescue. However, humans appear very small on the vast sea surface, and this poses a huge challenge in identifying them. In this study, a single-stage tiny person detector, namely the “You only look once”-based Maritime Tiny Person detector (MTP-YOLO), is proposed for detecting maritime tiny persons. Specifically, we designed the cross-stage partial layer with two convolutions Efficient Layer Aggregation Networks (C2fELAN) by drawing on the Generalized Efficient Layer Aggregation Networks (GELAN) of the latest YOLOv9, which preserves the key features of a tiny person during the calculations. Meanwhile, in order to accurately detect tiny persons in complex backgrounds, we adopted a Multi-level Cascaded Enhanced Convolutional Block Attention Module (MCE-CBAM) to make the network attach importance to the area where the object is located. Finally, by analyzing the sensitivity of tiny objects to position and scale deviation, we proposed a new object position regression cost function called Weighted Efficient Intersection over Union (W-EIoU) Loss. We verified our proposed MTP-YOLO on the TinyPersonv2 dataset. All these results confirm that this method significantly improves model performance while maintaining a low number of parameters and can therefore be applied to maritime emergency rescue missions.

The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A comparison with the performance of three other deep reinforcement learning algorithms reveals that the LSTM-PPO algorithm exhibits a 21.5% reduction in average convergence time, an 18.5% reduction in average path length, and an approximately 20% enhancement in the success rate of obstacle avoidance in complex environments. These results indicate that the LSTM-PPO algorithm can effectively enhance the search efficiency and optimize the path planning in obstacle avoidance for unmanned boats, rendering it more rational.

The reliability and safety of launch vehicle launch missions might be effectively increased thanks to the fault prediction and health management (PHM) technology of engines, which could also improve with problem diagnostics and decrease the cost of operation and maintenance overhaul. This paper combines the equipment characteristics and the current state of safeguarding for large, complex space systems, introduces the intelligent launch vehicle engine PHM technology methods that are being gradually implemented in space systems, and discusses and compares fault detection and health assessment techniques. Subsequently, analysis of the measurement signals from a rocket engine was performed using an example, and it was shown that the established comprehensive health assessment structure, which is based on the fault prediction algorithm method and the fuzzy comprehensive assessment method, could successfully realize the effectiveness of the rocket engine system health assessment, which had an outstanding application value.

Visual object detection is an essential task for the intelligent navigation of an Unmanned Surface Vehicle (USV), which can sense the obstacles while navigating. However, the harsh illumination conditions and large scale variation of the objects significantly harm the performance of object detection methods. To address the above problems, we propose a robust water surface object detection method named multi-scale feature fusion network with intrinsic decomposition generative adversarial network data augmentation (MFFDet-IDGAN). We introduce intrinsic decomposition as data augmentation for the object detection to achieve illumination adapting. And an intrinsic decomposition generative adversarial network (IDGAN) is proposed to achieve unsupervised intrinsic decomposition. Moreover, the multi-scale feature fusion network (MFFDet) adopts an improved bidirectional feature pyramid network (BiFPN) and spatial pyramid pooling (SPP) blocks to fuse features of different resolution for better multi-scale detection. And an improved weighted stochastic weight averaging (SWA) is proposed and applied in the training process to improve the generalization performance. We conduct extensive experiments on the Water Surface Object Detection Dataset (WSODD), and the results show that the proposed method can achieve 44% improvement over the baseline. And we further test our method on a real USV in the sailing process, the results show that our method can exceeding the baseline by 4.5%.

Sixty percent of the failures of launch vehicles in the ascending phase occur in the propulsion system. Among them, the vibration generated by the engine is an important factor in the occurrence of failure. At present, health assessment methods in the aerospace field are mostly for specific equipment, and scholars mostly assess the real-time health status of launch vehicle engines which can only reflect the current health status of the launch vehicle. Existing methods cannot be applied to different equipment, and there is a lack of research on health assessments of fuzzy and complex mechanical systems. In this article, we propose a multi-layer and multi-factor predictive evaluation method for a fuzzy and complex system and conduct experiments on real vibration data of rockets. First, we divide the health assessment level according to the vibration data that affect the normal operation of the rocket. Secondly, we obtain the future trend of vibration signals based on five data prediction methods and calculate the health status interval of the rocket engine’s working conditions based on the boxplot method. At the same time, we calculate the single health evaluation set of every vibration signal. We obtain the weights of each level and factor for the health value based on an analytic hierarchy process (AHP). The optimization of this step avoids an over-reliance on expert experience. Finally, we complete a fuzzy comprehensive evaluation of the engine system from the bottom up to obtain the final health value. The minimum evaluation error is 0.0193% on the test data of the Long March series launch vehicle engine, which shows that the proposed method can successfully predict and evaluate the launch vehicle engine.

The navigation obstacle avoidance method based on deep reinforcement learning has stronger adaptability and better performance compared to traditional algorithms in complex unknown dynamic environments, and has been widely developed and applied. However, when using multimodal information input, deep reinforcement learning strategy networks extract features that differ significantly between simulated and real world environments, resulting in poor algorithm output strategies and difficulty in transferring models obtained from simulation training to actual environments. To address the aforementioned issues, this article utilizes image segmentation to narrow the gap in environmental features, integrates multimodal information, and designs a deep reinforcement learning multimodal local obstacle avoidance algorithm, MMSEG-PPO, based on proximal strategy optimization algorithms. The algorithm is then ported to practical environments for deployment and testing. The experiment shows that the algorithm proposed in this article reduces the gap between the simulation environment and the actual environment, and has better performance and generalization when transplanted to the real world environment.