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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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Action quality assessment via moment aware network

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Evolving Systems 2025年第2期16卷 1-10页

作者： Han, Jifeng Zhang, Yanduo Lu, Tao Wang, Jiaming Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

Recent action quality assessment methods attempt to decompose the entire video into sub-actions of the same level, which still suffers from information redundancy and lacks certain interpretability. Through studying and analysing the evaluation rules of relevant sports events, we assume that certain critical moments in the actions will significantly impact the assessment of the entire action, if the weight of these critical moments in the evaluation process can be increased, then the results can be more accurate and more persuasive. To validate this idea, we propose a moment-aware approach for action quality assessment, a novel moment-aware module is used to explore the key moments in action globally and locally to obtain more reliable evaluation results. Extensive experiments have shown that our module captures key moments that are close to the focus of human referees, and our method has also achieved favorable results on multiple public AQA benchmarks, the Spearman’s rank correlation reached 0.9360 on FineDiving dataset and 0.8797 on AQA-7 dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Damage detection

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Exploration and Prospects of Large Language Models with ReAct Integration in Maritime Risk Identification

Exploration and Prospects of Large Language Models with ReAc...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Chen Shu, Zhong-Cheng Guo, Lei Chen, Yu-Han School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9789819639601

Maritime risk identification technology is more and more important for ships sailing on the sea, and can ensure the safety of ships sailing on the sea. However, traditional risk identification methods rely on historical data and qualitative experience, which is difficult to deal with complex and changeable environment. Recently, advancements in artificial intelligence, particularly large language models, have introduced new solutions to these challenges. While these models excel in data processing and pattern recognition, their effective application within the maritime domain remains problematic. This paper presents the ReAct (Reasoning and Acting) model, which enhances the adaptability of large language models by simulating human reasoning and actions. This paper first reviews the traditional methods and existing problems of maritime risk identification, and then discusses the basic principle of ReAct model and its potential in risk identification. Finally, the study proposes the integration of ReAct with large language models, highlighting the advantages and future prospects of this approach. This research aims to provide new theoretical and technological support for maritime risk identification, ultimately contributing to improved risk management and accident prevention. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Risk management

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Distributed Source Localization Under Anchor Position Uncertainty

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Chinese Journal of Electronics 2025年第1期23卷 93-96页

作者： Hongqing Liu Yuntao Wu Department of Electronic and Computer Engineering National University of Singapore Singapore Key Laboratory of Intelligent Robot in Hubei Province Wuhan Institute of Technology Wuhan China

In this work, a distributed source positioning approach is developed based on Alternating direction method of multipliers (ADMM). First, a centralized positioning method is developed under case of the anchor uncertainty. And then, the method is realized in a distributed way using ADMM. Simulation results show that the centralized one is robust to the anchor errors and distributed one has similar performance as the centralized one.

关键词： Location awareness Uncertainty Codes Simulation robot sensing systems Convex functions intelligent robots

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Accelerated inference for thyroid nodule recognition in ultrasound imaging using FPGA

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Physical and engineering sciences in medicine 2025年 2025 May 7页

作者： Wei Ma Xiaoxiao Wu Qing Zhang Xiang Li Xinglong Wu Jun Wang School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan P.R. China. School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan P.R. China. xwu@***. School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan P.R. China. wangjun@***.

Thyroid cancer is the most prevalent malignant tumour in the endocrine system, with its incidence steadily rising in recent years. Current central processing units (CPUs) and graphics processing units (GPUs) face significant challenges in terms of processing speed, energy consumption, cost, and scalability in the identification of thyroid nodules, making them inadequate for the demands of future green, efficient, and accessible healthcare. To overcome these limitations, this study proposes an efficient quantized inference method using a field-programmable gate array (FPGA). We employ the YOLOv4-tiny neural network model, enhancing software performance with the K-means + + optimization algorithm and improving hardware performance through techniques such as 8-bit weight quantization, batch normalization, and convolutional layer fusion. The study is based on the ZYNQ7020 FPGA platform. Experimental results demonstrate an average accuracy of 81.44% on the Tn3k dataset and 81.20% on the internal test set from a Chinese tertiary hospital. The power consumption of the FPGA platform, CPU (Intel Core i5-10200 H), and GPU (NVIDIA RTX 4090) were 3.119 watts, 45 watts, and 68 watts, respectively, with energy efficiency ratios of 5.45, 0.31, and 5.56. This indicates that the FPGA's energy efficiency is 17.6 times that of the CPU and 0.98 times that of the GPU. These results show that the FPGA not only significantly outperforms the CPU in speed but also consumes far less power than the GPU. Moreover, using mid-to-low-end FPGAs yields performance comparable to that of commercial-grade GPUs. This technology presents a novel solution for medical imaging diagnostics, with the potential to significantly enhance the speed, accuracy, and environmental sustainability of ultrasound image analysis, thereby supporting the future development of medical care.

关键词： FPGA Hardware design K-means++ Neural networks Thyroid cancer

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Two-Stage Hand-Eye Calibration based on Variance Minimization Principle

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Ding, Tao Tian, Dazhuang Wu, Hao Zhu, Dahu Li, Wenlog Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive Engineering Wuhan430070 China Wuhan University of Technology Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Provincial Engineering Research Center of Robot and Intelligent Manufacturing Wuhan430070 China Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan430074 China

Hand-eye calibration in industrial scenarios greatly determines the efficiency and effectiveness of 3D vision-guided robotic tasks. Most of the existing hand-eye calibration methods, however, rely on collecting large-scale point cloud samples to guarantee high measurement accuracy, which is time-consuming and particularly influenced by measurement defects such as local missing, noise, and uneven density. To overcome this challenging problem, we present a novel two-stage hand-eye calibration method based on the variance minimization principle. In the first stage, a robust function weighted variance minimization (RFWVM) registration algorithm is proposed for fine registration of defective point clouds, aiming at solving the coarse hand-eye calibration matrix with few-shot point clouds. In the second stage, an objective function of the calibration error correction algorithm is established via the point-to-plane distance variance minimization, which is intended to suppress the influence of measurement defects. Through these two stages, the globally optimal hand-eye matrix is solved by iteratively correcting the coarse hand-eye calibration matrix. A series of experiments validate the universality, accuracy, and robustness of the proposed method. © 1963-2012 IEEE.

关键词： Error correction

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TSM-Based Multi-pilot Behavior Recognition Method for Ship Bridges

TSM-Based Multi-pilot Behavior Recognition Method for Ship B...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Chen Wei, Yue-Nan Hu, Song-Tao Shu, Zhong-Cheng Zhao, Xin-Zheng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Henan Transport Investment Group Co. Ltd. Zhengzhou China

ISBN: (纸本)9789819639601

Abnormal or non-standard operations by ship drivers are one of the major factors leading to water traffic accidents, making the development of a real-time and reliable method for detecting ship driver behavior crucial. This paper proposes a novel approach for multi-person behavior recognition based on the Temporal Shift Module (TSM) algorithm for single-target behavior detection. This algorithm integrates multi-target tracking with single-target behavior recognition methods, achieving simultaneous recognition and detection of multiple drivers within the ship’s bridge. Additionally, a dataset named "SC Action" was created for ship bridge behavior, containing data samples from various ship bridge surveillance videos, including over 1,000 video samples of routine and violation behaviors. Experimental results show that this method, while accurately tracking multiple drivers, achieved a behavior recognition accuracy of 81.72% and is capable of providing early warnings of dangerous behaviors by drivers. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Ships

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ShipSegNet: A Contour-Driven Instance Segmentation Method for Maritime Ship Segmentation

ShipSegNet: A Contour-Driven Instance Segmentation Method fo...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Chen Hu, Song-Tao Wei, Yue-Nan Shu, Zhong-Cheng Liu, Liu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Henan Transport Investment Group Co. Ltd. Zhengzhou China

ISBN: (纸本)9789819639601

Ship instance segmentation technology is becoming more and more important in applications such as ship identification, monitoring and tracking, which is of great significance for maritime safety management. However, due to the different shapes of ships, as well as the complexity and volatility of illumination, weather and other factors, the existing ship instance segmentation methods are often difficult to achieve good segmentation results. To address this problem, this paper proposes a real-time segmentation method for ship instances based on contours that uses CenterNet algorithm to detect ship targets. The core network uses DLA-34 (Deep Layer Aggregation) to ensure the detection accuracy and speed. Then, the Deep Snake method is used to segment the ship object accurately. In order to verify the effectiveness of the proposed algorithm, this study constructed a dedicated dataset of 2300 images involving complex environments such as inland rivers and ports under three typical conditions: day, night, and haze. In addition, this dataset was used to test the proposed method, and the average recall (AR) rate was 95.5% and the average precision (AP) rate was 93.1%. The proposed method can achieve the inference performance of 47 frames per second on the RTX3090 GPU. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Ships

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Super-resolution reconstruction of remote sensing images based on multi-scale reparameterized pyramids

Super-resolution reconstruction of remote sensing images bas...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： RuiNan Huang GengSheng Zheng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In order to solve the problems of loss of structural details and insufficient attention to edge information in the existing lightweight remote sensing super-resolution reconstruction methods, a multi-scale reparameterized pyramid network (MRPN) method for remote sensing image super-resolution reconstruction based on multi-scale reparameterized pyramids was proposed. The model is based on the deep residual network structure, and multiple cascading multi-scale reparameterized pyramid modules are designed in the network for feature extraction. In the module, a multi-scale separation convolution and fusion module is designed to help the network refine image information, and the unglobal and global feature expressions are enhanced, and the link-enhanced spatial attention module is used to improve the attention to high-frequency feature information. Finally, the jumping connection method is used to fuse multi-level features to achieve effective spatial information fusion. The model is verified by 2x, 3x and 4x super-resolution reconstruction on the NWPU-RESISC45 dataset and the AID dataset, and the experimental results show that the peak signal-to-noise ratio and structural similarity are improved, and the method has good performance in improving image quality and preserving details.

关键词： PSNR Convolution Superresolution Semantics Reconstruction algorithms Feature extraction Visual effects Remote sensing Image reconstruction Residual neural networks

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