研究成果

2026

1. TMM

   

   OSRNet: Occlusion Recovery and Relationship Detection of Irregular-shaped Objects

   Qiaokang Liang, Xiangyu Zhang, Hai Qin, and 5 more authors

   IEEE Transactions on Multimedia, 2026

   DOI Bib PDF

   @article{11685390,
     author = {Liang, Qiaokang and Zhang, Xiangyu and Qin, Hai and Zhou, Li and Jiang, Weilai and Sun, Wei and Zhang, Dan and Wang, Yaonan},
     journal = {IEEE Transactions on Multimedia},
     title = {OSRNet: Occlusion Recovery and Relationship Detection of Irregular-shaped Objects},
     year = {2026},
     volume = {},
     number = {},
     pages = {1-13},
     keywords = {Modeling;Instance segmentation;Sorting;Training;Computers;Conferences;Computer vision;Shape;Grasping;Robots;Solid Waste;Occlusion;Recovery;Robotic sorting},
     doi = {10.1109/TMM.2026.3733023},
   }

2. TMM

   

   MM-SAG: Multi-Modal Semantic-Aware Graph for Multi-Label Image Classification of Kitchen Waste

   Hai Qin, Jintao Li, Qiaokang Liang, and 3 more authors

   IEEE Transactions on Multimedia, 2026

   DOI Bib PDF

   @article{11561075,
     author = {Qin, Hai and Li, Jintao and Liang, Qiaokang and Sun, Wei and Zhang, Dan and Wang, Yaonan},
     journal = {IEEE Transactions on Multimedia},
     title = {MM-SAG: Multi-Modal Semantic-Aware Graph for Multi-Label Image Classification of Kitchen Waste},
     year = {2026},
     volume = {},
     number = {},
     pages = {1-14},
     keywords = {Labeling;Modeling;Modules (abstract algebra);Multi label classification;Visualization;Image classification;Conferences;Learning (artificial intelligence);Training;Computer vision;Urban kitchen waste;Multi-label classification;Multi-modal;Multi-head attention},
     doi = {10.1109/TMM.2026.3703323},
   }

3. TASE

   

   HECE-IC: An Integrated Calibration Method for Delta Robot-Based Kitchen Waste Sorting Systems

   Hai Qin, Li Zhou, Songyun Deng, and 4 more authors

   IEEE Transactions on Automation Science and Engineering, 2026

   DOI Bib PDF

   @article{11458855,
     author = {Qin, Hai and Zhou, Li and Deng, Songyun and Zhang, Xiangyu and Liang, Qiaokang and Zhang, Dan and Wang, Yaonan},
     journal = {IEEE Transactions on Automation Science and Engineering},
     title = {HECE-IC: An Integrated Calibration Method for Delta Robot-Based Kitchen Waste Sorting Systems},
     year = {2026},
     volume = {23},
     number = {},
     pages = {7669-7681},
     keywords = {Integrated circuits;Filtering;Filters;Circuits and systems;Circuits;Central Processing Unit;Oscillators;Location awareness;Mobile communication;Electronic mail;Kitchen waste sorting;delta robot;integrated calibration;reconstruction error},
     doi = {10.1109/TASE.2026.3679514},
   }

4. TMECH

   

   An Efficient Continual Learning Grasp Detection Method With Lightweight RGB-D Fusion and Automated Data Annotation for Kitchen Waste Sorting

   Songyun Deng, Yaoxin Lai, Li Zhou, and 7 more authors

   IEEE/ASME Transactions on Mechatronics, 2026

   DOI Bib PDF

   @article{11362985,
     author = {Deng, Songyun and Lai, Yaoxin and Zhou, Li and Pei, Ruiyuan and Qin, Hai and Xiang, Shao and Liang, Qiaokang and Sun, Wei and Zhang, Dan and Wang, Yaonan},
     journal = {IEEE/ASME Transactions on Mechatronics},
     title = {An Efficient Continual Learning Grasp Detection Method With Lightweight RGB-D Fusion and Automated Data Annotation for Kitchen Waste Sorting},
     year = {2026},
     volume = {},
     number = {},
     pages = {1-12},
     keywords = {Continuing education;Sorting;Grasping;Robots;Feature extraction;Accuracy;Robot kinematics;Grippers;Computational efficiency;Training;Automated data collection;continual learning;generative grasp model;kitchen waste sorting;RGB-D fusion},
     doi = {10.1109/TMECH.2025.3650331},
   }

2025

1. CAC

   

   Underwater Object Detection Using Improved YOLOv8 Model Based on Efficient Multi-Scale Adaptive Fusion

   Liyuan Liu, Xinpu Shen, Xiujing Gao, and 3 more authors

   In 2025 China Automation Congress (CAC), 2025

   DOI Bib PDF

   @inproceedings{11487456,
     author = {Liu, Liyuan and Shen, Xinpu and Gao, Xiujing and Qin, Hai and Liang, Qiaokang and Wang, Yaonan},
     booktitle = {2025 China Automation Congress (CAC)},
     title = {Underwater Object Detection Using Improved YOLOv8 Model Based on Efficient Multi-Scale Adaptive Fusion},
     year = {2025},
     volume = {},
     number = {},
     pages = {800-805},
     keywords = {Filtering;Filters;Pixel;Protocols;HTTP;Digital images;Autonomous underwater vehicles;Autonomous vehicles;Learning (artificial intelligence);Machine learning;Underwater Object Detection;Multi-Scale Adaptive Fusion;Gated Low-Level Detection;Wise-IoU Loss;YOLOv8},
     doi = {10.1109/CAC67268.2025.11487456},
   }

2. TMECH

   

   F-VIS: Efficient Robotic Tactile Sensing via Fiber Bragg Grating-Based Force and Vision Fusion

   Qiaokang Liang, Xiaoyu Yuan, Jianyong Long, and 1 more author

   IEEE/ASME Transactions on Mechatronics, 2025

   DOI Bib HTML PDF

   @article{11322578,
     author = {Liang, Qiaokang and Yuan, Xiaoyu and Long, Jianyong and Zhang, Dan},
     journal = {IEEE/ASME Transactions on Mechatronics},
     title = {F-VIS: Efficient Robotic Tactile Sensing via Fiber Bragg Grating-Based Force and Vision Fusion},
     year = {2025},
     volume = {},
     number = {},
     pages = {1-11},
     keywords = {Robot sensing systems;Force;Force measurement;Tactile sensors;Cameras;Optical fibers;Optical fiber sensors;Imaging;Fiber gratings;Lenses;Depth reconstruction;direct force measurement;fiber Bragg grating (FBG);visual–tactile sensor;without marker layers},
     doi = {10.1109/TMECH.2025.3643922},
   }

3. Sens. J

   

   Robotic Grasping Detection Based on Continual Learning Using Perceptual Loss and Multibranch Deep Fusion

   Qiaokang Liang, Yaoxin Lai, Songyun Deng, and 3 more authors

   IEEE Sensors Journal, 2025

   DOI Bib PDF

   @article{11237009,
     author = {Liang, Qiaokang and Lai, Yaoxin and Deng, Songyun and Chen, Xinhao and Yuan, Xiaoyu and Zhou, Li},
     journal = {IEEE Sensors Journal},
     title = {Robotic Grasping Detection Based on Continual Learning Using Perceptual Loss and Multibranch Deep Fusion},
     year = {2025},
     volume = {25},
     number = {24},
     pages = {44962-44972},
     keywords = {Grasping;Continuing education;Visualization;Robot sensing systems;Robot kinematics;Sensors;Adaptation models;Accuracy;Feature extraction;Training;Continual learning;grasp detection;RGB-D fusion;RGB-D sensors;robotic grasp},
     doi = {10.1109/JSEN.2025.3628829},
   }

4. UMST

   

   OSRPC: 基于流数据混合采样的主动学习餐厨垃圾检测

   梁桥康, 郑正月, 舒立业, and 1 more author

   无人系统技术, 2025

   DOI Bib PDF

   @article{UMST202503006,
     author = {梁桥康 and 郑正月 and 舒立业 and 秦海},
     title = {OSRPC: 基于流数据混合采样的主动学习餐厨垃圾检测},
     journal = {无人系统技术},
     volume = {8},
     number = {03},
     pages = {80-88},
     year = {2025},
     issn = {2096-5915},
     doi = {10.19942/j.issn.2096-5915.2025.03.26},
   }

5. TMECH

   

   A Multimodal Robust Recognition Method for Grasping Objects With Robot Flexible Grippers

   Qiaokang Liang, Wenxing Xiao, Jianyong Long, and 1 more author

   IEEE/ASME Transactions on Mechatronics, 2025

   DOI Bib HTML PDF

   @article{10606094,
     author = {Liang, Qiaokang and Xiao, Wenxing and Long, Jianyong and Zhang, Dan},
     journal = {IEEE/ASME Transactions on Mechatronics},
     title = {A Multimodal Robust Recognition Method for Grasping Objects With Robot Flexible Grippers},
     year = {2025},
     volume = {30},
     number = {2},
     pages = {1154-1165},
     keywords = {Grasping;Grippers;Robot sensing systems;Robots;Fiber gratings;Target recognition;Task analysis;Fiber Bragg grating (FBG);inertial measurement unit (IMU);multiflexible gripper;multimodal perception;robust object recognition},
     doi = {10.1109/TMECH.2024.3419420},
   }

6. TIM

   

   FBG-FT-IMU: Multimodal Measurement System for Acupuncture Manipulation

   Qiaokang Liang, Zhanjin Deng, Jianyong Long, and 5 more authors

   IEEE Transactions on Instrumentation and Measurement, 2025

   DOI Bib HTML PDF

   @article{10926497,
     author = {Liang, Qiaokang and Deng, Zhanjin and Long, Jianyong and Wenxing, Xiao and Peng, Jianqing and Pan, Xingfang and Ren, Haiyan and Wang, Yaonan},
     journal = {IEEE Transactions on Instrumentation and Measurement},
     title = {FBG-FT-IMU: Multimodal Measurement System for Acupuncture Manipulation},
     year = {2025},
     volume = {74},
     number = {},
     pages = {1-10},
     keywords = {Acupuncture;Sensors;Force measurement;Force;Needles;Fiber gratings;Torque measurement;Torque;Kinematics;Force sensors;Acupuncture manipulation;fiber Bragg grating (FBG);force/torque sensors;inertial measurement unit (IMU);multimodal measurement},
     doi = {10.1109/TIM.2025.3551443},
   }

7. TIM

   

   An Efficient Generative Intelligent Multiobjective Grasping Model for Kitchen Waste Sorting

   Songyun Deng, Ruiyuan Pei, Li Zhou, and 3 more authors

   IEEE Transactions on Instrumentation and Measurement, 2025

   DOI Bib HTML PDF

   @article{10945475,
     author = {Deng, Songyun and Pei, Ruiyuan and Zhou, Li and Qin, Hai and Sun, Wei and Liang, Qiaokang},
     journal = {IEEE Transactions on Instrumentation and Measurement},
     title = {An Efficient Generative Intelligent Multiobjective Grasping Model for Kitchen Waste Sorting},
     year = {2025},
     volume = {74},
     number = {},
     pages = {1-10},
     keywords = {Grasping;Belts;Robots;Sorting;Annotations;Computational modeling;Accuracy;Planning;Robot kinematics;Object detection;Embedded AI;generative network;kitchen waste sorting;multiobject grasping;RGB-D dataset},
     doi = {10.1109/TIM.2025.3555698},
   }

8. TIM

   

   MCS-ResNet: A Generative Robot Grasping Network Based on RGB-D Fusion

   Ruiyuan Pei, Songyun Deng, Li Zhou, and 2 more authors

   IEEE Transactions on Instrumentation and Measurement, 2025

   DOI Bib HTML PDF

   @article{10816048,
     author = {Pei, Ruiyuan and Deng, Songyun and Zhou, Li and Qin, Hai and Liang, Qiaokang},
     journal = {IEEE Transactions on Instrumentation and Measurement},
     title = {MCS-ResNet: A Generative Robot Grasping Network Based on RGB-D Fusion},
     year = {2025},
     volume = {74},
     number = {},
     pages = {1-12},
     keywords = {Grasping;Robots;Accuracy;Robot kinematics;Robot vision systems;Waste materials;Shape;Cameras;Solid modeling;Grippers;Generative network;grasp;grasp detection;RGB and Depth (RGB-D) dataset;RGB-D fusion;robot},
     doi = {10.1109/TIM.2024.3522430},
   }

2024

1. TMECH

   

   A Novel Fiber Bragg Grating Three-Dimensional Force Sensor for Medical Robotics

   Qiaokang Liang, Songtao Ouyang, Jianyong Long, and 2 more authors

   IEEE/ASME Transactions on Mechatronics, 2024

   DOI Bib HTML PDF

   @article{10557466,
     author = {Liang, Qiaokang and Ouyang, Songtao and Long, Jianyong and Zhou, Li and Zhang, Dan},
     journal = {IEEE/ASME Transactions on Mechatronics},
     title = {A Novel Fiber Bragg Grating Three-Dimensional Force Sensor for Medical Robotics},
     year = {2024},
     volume = {},
     number = {},
     pages = {1-11},
     keywords = {Robot sensing systems;Force;Force sensors;Springs;Deformation;Sensitivity;Medical robotics;Fiber Bragg Grating (FBG);medical robots;structure self-decoupling;three-dimensional (3-D) force sensor},
     doi = {10.1109/TMECH.2024.3408427},
   }

2. TIM

   

   Active Learning-DETR: Cost-Effective Object Detection for Kitchen Waste

   Hai Qin, Liye Shu, Li Zhou, and 6 more authors

   IEEE Transactions on Instrumentation and Measurement, 2024

   DOI Bib HTML PDF

   @article{10443710,
     author = {Qin, Hai and Shu, Liye and Zhou, Li and Deng, Songyun and Xiao, Haihua and Sun, Wei and Liang, Qiaokang and Zhang, Dan and Wang, Yaonan},
     journal = {IEEE Transactions on Instrumentation and Measurement},
     title = {Active Learning-DETR: Cost-Effective Object Detection for Kitchen Waste},
     year = {2024},
     volume = {73},
     number = {},
     pages = {1-15},
     keywords = {Sorting;Object detection;Robots;Transformers;Plastic products;Visualization;Training;Active learning (AL);kitchen waste images;object detection;robotic sorting;transformer},
     doi = {10.1109/TIM.2024.3368494},
   }

3. TAI

   

   Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform

   Songyun Deng, Wanneng Wu, Kunlin Zou, and 3 more authors

   IEEE Transactions on Artificial Intelligence, 2024

   DOI Bib HTML PDF

   @article{10227525,
     author = {Deng, Songyun and Wu, Wanneng and Zou, Kunlin and Qin, Hai and Cheng, Lekai and Liang, Qiaokang},
     journal = {IEEE Transactions on Artificial Intelligence},
     title = {Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform},
     year = {2024},
     volume = {5},
     number = {5},
     pages = {2040-2054},
     keywords = {Plant diseases;Training;Computational modeling;Deep learning;Computer architecture;Real-time systems;Image recognition;AMI-NanoNet;ensemble learning;Jetson Nano;lightweight network;plant disease identification},
     doi = {10.1109/TAI.2023.3307662},
   }

2023

1. 

   光纤Bragg光栅力觉传感器在健康医疗领域的应用

   李红豪, 龙建勇, 成乐凯, and 1 more author

   测控技术, Apr 2023

   Bib PDF

   @article{李红豪 2023 光纤Bragg光栅力觉传感器在健康医疗领域的应用,
     author = {李红豪 and 龙建勇 and 成乐凯 and 梁桥康},
     title = { 光纤Bragg光栅力觉传感器在健康医疗领域的应用 },
     organization = {湖南大学电气与信息工程学院 and 湖南大学电气与信息工程学院},
     journal = {测控技术},
     year = {2023},
     volume = {42},
     number = {4},
     pages = {9-21},
     month = apr,
   }

2019

1. JBHI

   

   Weakly Supervised Biomedical Image Segmentation by Reiterative Learning

   Qiaokang Liang, Yang Nan, Gianmarc Coppola, and 5 more authors

   IEEE Journal of Biomedical and Health Informatics, Apr 2019

   DOI Bib HTML

   @article{8394987,
     author = {Liang, Qiaokang and Nan, Yang and Coppola, Gianmarc and Zou, Kunglin and Sun, Wei and Zhang, Dan and Wang, Yaonan and Yu, Guanzhen},
     journal = {IEEE Journal of Biomedical and Health Informatics},
     title = {Weakly Supervised Biomedical Image Segmentation by Reiterative Learning},
     year = {2019},
     volume = {23},
     number = {3},
     pages = {1205-1214},
     keywords = {Image segmentation;Biomedical imaging;Cancer;Training;Biological system modeling;Machine learning;Object segmentation;Reiterative learning;gastric histopathology;deep neural networks;biomedical image segmentation},
     doi = {10.1109/JBHI.2018.2850040},
   }

2018

1. RCIM

   

   Calibration and decoupling of multi-axis robotic Force/Moment sensors

   Qiaokang Liang, Wanneng Wu, Gianmarc Coppola, and 4 more authors

   Robotics and Computer-Integrated Manufacturing, Apr 2018

   Abs DOI Bib HTML PDF

   Multi-axis robotic Force/Moment (F/M) sensors are capable of simultaneously detecting multiple components of force (Fx, Fy, and Fz), as well as the moments (Mx, My and Mz). This enables them to be frequently used in many robotic applications. Accurate, time-effective calibration and decoupling procedures are critical to the implementation of these sensors. This paper compares the effectiveness of decoupling methods based on Least-Squares (LS), BP Neural Network (BPNN), and Extreme Learning Machine (ELM) methods for improving the performance of multi-axis robotic F/M sensors. In order to demonstrate the effectiveness of the decoupling methods, a calibration and decoupling experiment was performed on a five-axis robotic F/M sensor. The experiments demonstrate that the ELM based decoupling method is superior to LS and BPNN based methods. The presented theoretical and experimental demonstrations provide a comprehensive description of the calibration and decoupling procedures of multi-axis robotic F/M sensors. This work reveals that the ELM method is an appropriate and high performing decoupling procedure for multi-axis robotic F/M sensors.

   @article{LIANG2018301,
     title = {Calibration and decoupling of multi-axis robotic Force/Moment sensors},
     journal = {Robotics and Computer-Integrated Manufacturing},
     volume = {49},
     pages = {301-308},
     year = {2018},
     issn = {0736-5845},
     doi = {https://doi.org/10.1016/j.rcim.2017.08.008},
     url = {https://www.sciencedirect.com/science/article/pii/S0736584516301867},
     author = {Liang, Qiaokang and Wu, Wanneng and Coppola, Gianmarc and Zhang, Dan and Sun, Wei and Ge, Yunjian and Wang, Yaonan},
     keywords = {Robotic sensory system, Multi-axis Force/Moment sensors, Calibration and decoupling, Extreme Learning Machine},
     dimensions = {true},
   }

2. ICIA

   

   Adaptive Detection Scheme for Solder Joints Based on Guided Image Filtering and Geometrical Edge Selection

   Qiaokang Liang, Jing jin, Jianyong Long, and 4 more authors

   In 2018 IEEE International Conference on Information and Automation (ICIA), Apr 2018

   DOI Bib HTML

   @inproceedings{8812567,
     author = {Liang, Qiaokang and jin, Jing and Long, Jianyong and Sun, Wei and Cai, Juan and Xia, Xiaohu and Zhang, Guangbing},
     booktitle = {2018 IEEE International Conference on Information and Automation (ICIA)},
     title = {Adaptive Detection Scheme for Solder Joints Based on Guided Image Filtering and Geometrical Edge Selection},
     year = {2018},
     volume = {},
     number = {},
     pages = {1042-1047},
     keywords = {Image edge detection;Soldering;Interference;Calibration;Inspection;Clustering algorithms;Image filtering;Solder joint;the homomorphic filtering;the guidance image filtering;geometrical edges selection;K-means clustering},
     doi = {10.1109/ICInfA.2018.8812567},
   }

3. WCICA

   

   Apple recognition based on Convolutional Neural Network Framework

   Qiaokang Liang, Jianyong Long, Wei Zhu, and 2 more authors

   In 2018 13th World Congress on Intelligent Control and Automation (WCICA), Apr 2018

   DOI Bib HTML

   @inproceedings{8630705,
     author = {Liang, Qiaokang and Long, Jianyong and Zhu, Wei and Wang, Yaonan and Sun, Wei},
     booktitle = {2018 13th World Congress on Intelligent Control and Automation (WCICA)},
     title = {Apple recognition based on Convolutional Neural Network Framework},
     year = {2018},
     volume = {},
     number = {},
     pages = {1751-1756},
     keywords = {Feature extraction;Convolution;Robots;Object detection;Training;Task analysis;Real-time systems},
     doi = {10.1109/WCICA.2018.8630705},
   }