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学者姓名：罗伟林
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Abstract ：
A deep learning based underwater object detection model is proposed to address the problem of low detection accuracy of multiple underwater targets, which is due to the blurred underwater environments and the fusion between underwater targets and background. To improve the receptive field and subtract more effective topmost feature, the YOLOv5 detector is modified by adding deformable convolution and dilated convolution. Further, Swin Transformer is introduced into the backbone of the modified YOLOv5 to obtain more information of small underwater objects. Instead of coupled detection head, a decoupled head is designed in the modified YOLOv5 to reduce the interference between data by predicting classification, regression and confidence information separately. Combined with an underwater dataset, the improved underwater multi-objective detection model is verified. The detection results show that the accuracy of the model for small underwater target detection has been significantly improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Convolution Convolution Deep learning Deep learning Object detection Object detection Object recognition Object recognition Signal detection Signal detection
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| GB/T 7714 | Luo, Weilin , Lin, Chengyu , Zhou, Huan . Underwater Multi-objective Detection by Using Swin Transformer Based YOLO Network [C] . 2025 : 278-288 . |
| MLA | Luo, Weilin 等. "Underwater Multi-objective Detection by Using Swin Transformer Based YOLO Network" . (2025) : 278-288 . |
| APA | Luo, Weilin , Lin, Chengyu , Zhou, Huan . Underwater Multi-objective Detection by Using Swin Transformer Based YOLO Network . (2025) : 278-288 . |
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Abstract ：
The performance of underwater target detection techniques is limited by various factors. First, in underwater image data sets, there exists degradation such as color bias, low contrast, and blurring, which affect the accuracy of the detection algorithms. Second, the underwater image data set is difficult to obtain and the cost of making the labeled data sets is high, which also prevents underwater object detection algorithms from fully leveraging their potential. In this article, we propose a self-supervised learning network for underwater target detection. Considering the deficiency that the conventional contrastive learning network pays more attention to the global information and ignores the local information, an auxiliary branch inspired by masked autoencoders is added to the baseline SimSiam network, which collaborates with the main branch to optimize the target network and help the target network learn the local information of the target feature map. A residual spatial cooperative attention module is proposed to be embedded within the proposed self-supervised learning network to obtain remote information through residual structure and construct spatial context features. The method of cooperative attention is used to enhance feature learning ability. Experiments are carried out on a reconstructed underwater target data set. Results show that compared with the baseline network, the method proposed in this article is more suitable for underwater environments and has better mean average precision.
Keyword ：
Attention module Attention module autoencoder autoencoder image augmentation image augmentation self-supervised learning self-supervised learning underwater target detection underwater target detection
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| GB/T 7714 | Luo, Weilin , Lin, Chengyu , Zhou, Huan . Underwater Target Detection by Residual Spatial Cooperative Attention Module-Based Self-Supervised Learning [J]. | IEEE JOURNAL OF OCEANIC ENGINEERING , 2025 . |
| MLA | Luo, Weilin 等. "Underwater Target Detection by Residual Spatial Cooperative Attention Module-Based Self-Supervised Learning" . | IEEE JOURNAL OF OCEANIC ENGINEERING (2025) . |
| APA | Luo, Weilin , Lin, Chengyu , Zhou, Huan . Underwater Target Detection by Residual Spatial Cooperative Attention Module-Based Self-Supervised Learning . | IEEE JOURNAL OF OCEANIC ENGINEERING , 2025 . |
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This paper presents a method for optimizing the multidisciplinary shape design of underwater vehicles using a dynamic proxy model. The method employs a collaborative optimization approach that considers various disciplines, including rapidity, maneuverability, energy consumption, and structural strength of the underwater vehicle. The K and T indices are effectively utilized to represent the maneuverability performance of underwater vehicles. The hydrodynamics of underwater vehicles are analyzed using the Computational Fluid Dynamics (CFD) numerical simulation method. To reduce the computational burden in the optimization loop, this paper proposes a dynamic proxy model that combines the trust region with the adaptive minimum confidence Lowest Credible Bound (LCB) and the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm. Additionally, an adaptive balance constant is introduced into the proxy model. The collaborative optimization framework employs a combined optimization algorithm based on the genetic algorithm and Nonlinear Programming by Quadratic Lagrangian Programming (NLPQLP) algorithm. The results of applying this optimization strategy to the SUBOFF model demonstrate its effectiveness in optimizing the resistance, mass, maneuverability, structural strength, and energy consumption of the underwater vehicle.
Keyword ：
Collaborative optimization Collaborative optimization Dynamic proxy model Dynamic proxy model Hydrodynamics Hydrodynamics K and T indices K and T indices Underwater vehicle Underwater vehicle
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| GB/T 7714 | Sun, Shaojun , Luo, Weilin . Multi-disciplinary optimization of underwater vehicles based on a dynamic proxy model [J]. | BRODOGRADNJA , 2025 , 76 (3) . |
| MLA | Sun, Shaojun 等. "Multi-disciplinary optimization of underwater vehicles based on a dynamic proxy model" . | BRODOGRADNJA 76 . 3 (2025) . |
| APA | Sun, Shaojun , Luo, Weilin . Multi-disciplinary optimization of underwater vehicles based on a dynamic proxy model . | BRODOGRADNJA , 2025 , 76 (3) . |
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This paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation-based parameterized modeling is used to express the airfoil. The Wilson method is employed to obtain the aerodynamic shape of the blade. Computational fluid dynamics numerical simulation is performed to analyze the aerodynamics of the blade. In the structural discipline, the materials and ply lay-up design are studied. Finite element method-based modal analysis and static structural analysis are conducted to verify the structural design of the blade. A collaborative optimization framework is set up on the Isight platform, employing a genetic algorithm to find the optimal solution for the blade's aerodynamics and structural properties. In the optimization framework, the design variables refer to the length of the blade chord, twist angle, and lay-up thickness. Additionally, Kriging surrogate models are constructed to reduce the numerical simulation time required during optimization. An optimal Latin hypercube sampling method-based experimental design is employed to determine the samples used in the surrogate models. The optimized blade exhibits improved performance in both the aerodynamic and the structural disciplines.
Keyword ：
aerodynamic characteristics aerodynamic characteristics multidisciplinary design optimization multidisciplinary design optimization numerical simulation numerical simulation structural characteristics structural characteristics wind turbine blades wind turbine blades
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| GB/T 7714 | He, Fushan , Zheng, Xingsheng , Luo, Weilin et al. Collaborative Optimization of Aerodynamics and Wind Turbine Blades [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (2) . |
| MLA | He, Fushan et al. "Collaborative Optimization of Aerodynamics and Wind Turbine Blades" . | APPLIED SCIENCES-BASEL 15 . 2 (2025) . |
| APA | He, Fushan , Zheng, Xingsheng , Luo, Weilin , Zhong, Jianfeng , Huang, Yunhua , Ye, Aili et al. Collaborative Optimization of Aerodynamics and Wind Turbine Blades . | APPLIED SCIENCES-BASEL , 2025 , 15 (2) . |
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To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance.
Keyword ：
collaborative optimization collaborative optimization energy consumption energy consumption proxy model proxy model resistance resistance underwater vehicle underwater vehicle
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| GB/T 7714 | Sun, Shaojun , Luo, Weilin . Multidisciplinary Design Optimization of Underwater Vehicles Based on a Combined Proxy Model [J]. | JOURNAL OF MARINE SCIENCE AND ENGINEERING , 2024 , 12 (7) . |
| MLA | Sun, Shaojun et al. "Multidisciplinary Design Optimization of Underwater Vehicles Based on a Combined Proxy Model" . | JOURNAL OF MARINE SCIENCE AND ENGINEERING 12 . 7 (2024) . |
| APA | Sun, Shaojun , Luo, Weilin . Multidisciplinary Design Optimization of Underwater Vehicles Based on a Combined Proxy Model . | JOURNAL OF MARINE SCIENCE AND ENGINEERING , 2024 , 12 (7) . |
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ABSTRACT The lines optimisation of an underwater vehicle based on a dynamic surrogate model is studied. Four performances including rapidity, manoeuverability, energy consumption and structure of the underwater vehicle are considered in the optimisation framework constructed by a generalised collaborative optimisation method. Expert knowledge based analytic hierarchy process is conducted to obtain the optimisation object that involves the four performances. Numerical simulation is performed to accurately analyze the rapidity, manoeuverability and structure performances of the underwater vehicle. To reduce the calculation burden, dynamic surrogate models are proposed to replace numerical simulation in the optimisation framework. To guarantee the optimisation efficiency and accuracy, a synthetic minority oversampling technique (SMOTE) and adaptive minimise lower confidence bound (LCB) are combined in constructing the dynamic surrogate models. The proposed optimisation strategy is applied to the SUBOFF model and compared with other dynamic surrogate models. Comparison results prove the advantages of the proposed dynamic surrogate model.
Keyword ：
analytic hierarchy process analytic hierarchy process dynamic surrogate model dynamic surrogate model lower confidence bound lower confidence bound MDO MDO synthetic minority oversampling technique synthetic minority oversampling technique
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| GB/T 7714 | Weifeng Pan , Weilin Luo . Lines optimisation of an underwater vehicle using SMOTE and adaptive minimise LCB based dynamic surrogate models [J]. | Ships and Offshore Structures , 2024 , 19 (1) : 91-108 . |
| MLA | Weifeng Pan et al. "Lines optimisation of an underwater vehicle using SMOTE and adaptive minimise LCB based dynamic surrogate models" . | Ships and Offshore Structures 19 . 1 (2024) : 91-108 . |
| APA | Weifeng Pan , Weilin Luo . Lines optimisation of an underwater vehicle using SMOTE and adaptive minimise LCB based dynamic surrogate models . | Ships and Offshore Structures , 2024 , 19 (1) , 91-108 . |
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Based on deep learning, an underwater image instance segmentation method is proposed. Firstly, in view of the scarcity of underwater related data sets, the size of the data set is expanded by measures including image rotation and flipping, and image generation by a generative adversarial network (GAN). Next, the underwater image data set is finally constructed by manual labeling. Then, in order to solve the problems of color shift, blur and the poor contrast of optical images caused by the complex underwater environment and the attenuation and scattering of light, an underwater image enhancement algorithm is used to first preprocess the data set, and several algorithms are discussed, including multi-scale Retinex (MSRCR) with color recovery, integrated color model (ICM), relative global histogram stretching (RGHS) and unsupervised color correction (UCM), as well as the color shift removal proposed in this work. Specifically, the results indicate that the proposed method can largely increase the segmentation mAP (mean average precision) by 85.7% compared with without the pretreatment method. In addition, based on the characteristics of the constructed underwater dataset, the feature pyramid network (FPN) is improved to some extent, and the preprocessing method is further combined with the improved network for experiments and compared with other neural networks to verify the effectiveness of the proposed method, thus achieving the effect and purpose of improving underwater image instance segmentation and target recognition. The experimental analysis results show that the proposed model can achieve a mAP of 0.245, which is about 1.1 times higher than other target recognition models.
Keyword ：
data augmentation data augmentation deep learning deep learning image enhancement image enhancement instance segmentation instance segmentation underwater image underwater image
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| GB/T 7714 | Chen, Jianfeng , Zhu, Shidong , Luo, Weilin . Instance Segmentation of Underwater Images by Using Deep Learning [J]. | ELECTRONICS , 2024 , 13 (2) . |
| MLA | Chen, Jianfeng et al. "Instance Segmentation of Underwater Images by Using Deep Learning" . | ELECTRONICS 13 . 2 (2024) . |
| APA | Chen, Jianfeng , Zhu, Shidong , Luo, Weilin . Instance Segmentation of Underwater Images by Using Deep Learning . | ELECTRONICS , 2024 , 13 (2) . |
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Navigating through complex flow fields, underwater vehicles often face insufficient thrust to traverse particularly strong current areas, necessitating consideration of the physical feasibility of paths during route planning. By constructing a flow field database through Computational Fluid Dynamics (CFD) simulations of the operational environment, we were able to analyze local uncertainties within the flow field. Our investigation into path planning using these flow field data has led to the proposal of a hierarchical planning strategy that integrates global sampling with local optimization, ensuring both completeness and optimality of the planner. Initially, we developed an improved global sampling algorithm derived from RRT to attain nearly optimal theoretical feasible solutions on a global scale. Subsequently, we implemented corrective measures using directed expansion to address locally infeasible sections. The algorithm's efficacy was theoretically validated, and simulated results based on real flow field environments were provided.
Keyword ：
computational fluid dynamics computational fluid dynamics flow fields flow fields path planning path planning rapidly-exploring random trees rapidly-exploring random trees underwater vehicle underwater vehicle
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| GB/T 7714 | Jin, Fengqiao , Cheng, Bo , Luo, Weilin . Data-Driven Based Path Planning of Underwater Vehicles Under Local Flow Field [J]. | JOURNAL OF MARINE SCIENCE AND ENGINEERING , 2024 , 12 (12) . |
| MLA | Jin, Fengqiao et al. "Data-Driven Based Path Planning of Underwater Vehicles Under Local Flow Field" . | JOURNAL OF MARINE SCIENCE AND ENGINEERING 12 . 12 (2024) . |
| APA | Jin, Fengqiao , Cheng, Bo , Luo, Weilin . Data-Driven Based Path Planning of Underwater Vehicles Under Local Flow Field . | JOURNAL OF MARINE SCIENCE AND ENGINEERING , 2024 , 12 (12) . |
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For the trajectory tracking of unsymmetric underactuated autonomous underwater vehicle (AUV), a neural network (NN) and disturbance observer-based strategy is proposed. Disturbance and input saturation are considered in the dynamics of AUV. Diffeomorphism transformation is employed to obtain an equivalent system to the original unsymmetric system. To deal with the underactuation, an improved approach angle is proposed and an additional control is designed to stabilise the velocity error in the underactuated sway motion. To deal with the external disturbance, an observer with guaranteed convergence is incorporated into the dynamics controller. To deal with the input constraint, adaptive neural networks are designed to identify the errors induced by input saturation. To avoid the calculation of time derivatives of virtual velocities, command filters are employed. Numerical simulation is performed to verify the effectiveness of the proposed control strategy. Under the proposed controller, both straight line and curve trajectories can be tracked well.
Keyword ：
additional control additional control disturbance observer disturbance observer neural networks neural networks robust control of nonlinear systems robust control of nonlinear systems Underactuated underwater vehicle Underactuated underwater vehicle
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| GB/T 7714 | Luo, Weilin , Wang, Xincheng . Neural network and disturbance observer-based practical trajectory tracking of unsymmetric underactuated AUV with disturbance and input saturation [J]. | SHIPS AND OFFSHORE STRUCTURES , 2024 , 20 (7) : 1004-1015 . |
| MLA | Luo, Weilin et al. "Neural network and disturbance observer-based practical trajectory tracking of unsymmetric underactuated AUV with disturbance and input saturation" . | SHIPS AND OFFSHORE STRUCTURES 20 . 7 (2024) : 1004-1015 . |
| APA | Luo, Weilin , Wang, Xincheng . Neural network and disturbance observer-based practical trajectory tracking of unsymmetric underactuated AUV with disturbance and input saturation . | SHIPS AND OFFSHORE STRUCTURES , 2024 , 20 (7) , 1004-1015 . |
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In this paper, the lines design of an underwater vehicle is investigated. Optimization design is used to obtain the optimal lines. The underwater vehicle's hydrodynamic performances and energy consumption are involved in the framework of optimization. Specifically, the resistance and maneuverability of an underwater vehicle are concerned in the hydrodynamic performances. The energy consumption of the underwater vehicle concerns the efficient power. To weight the disciplines in the overall optimization objective, analytic hierarchy process (AHP) is used. To replace the numerical simulation model in discipline analysis, an approximate model is established. SUBOFF model is optimized by using the proposed method. Optimization results reveal the effectiveness of proposed method. © 2024 IEEE.
Keyword ：
Energy utilization Energy utilization Hierarchical systems Hierarchical systems Maneuverability Maneuverability
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| GB/T 7714 | Luo, Weilin , Chen, Leilei , Sun, Shaojun et al. Lines Design of Underwater Vehicle Using Analytic Hierarchy Process and Approximation Model [C] . 2024 : 372-376 . |
| MLA | Luo, Weilin et al. "Lines Design of Underwater Vehicle Using Analytic Hierarchy Process and Approximation Model" . (2024) : 372-376 . |
| APA | Luo, Weilin , Chen, Leilei , Sun, Shaojun , Pan, Weifeng . Lines Design of Underwater Vehicle Using Analytic Hierarchy Process and Approximation Model . (2024) : 372-376 . |
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