基本信息
吕彦锋  男  硕导  中国科学院自动化研究所
电子邮件: yanfeng.lv@ia.ac.cn
通信地址: 北京市中关村东路95号
邮政编码: 100190

招生信息


欢迎申请推免硕士,欢迎报考硕士研究生!(申请:“人工智能菁英班”)

欢迎报考国科大人工智能学院 非全日制研究生!

邮箱:yanfeng.lv@ia.ac.cn


个人简介

吕彦锋,多模态人工智能系统全国重点实验室副研究员、中国科学院特聘研究骨干,国家公派留学博士。主要从事机器人智能感知与学习、类脑感认知、计算机视觉等研究,近年来在IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Intelligent Transportation Systems、IEEE Transactions on Cognitive and Developmental Systems等国际期刊会议发表学术论文四十余篇,主持了科技创新2030“新一代人工智能”、农业科技重大项目、国家自然科学基金、北京市自然科学基金等科研任务十余项,是中国计算机学会数字农业分会执行委员、中国自动化学会混合智能专委委员、中国图象图形学会交通视频专委委员。


主要从事:类脑感认知、图像检测与识别、器人多模态感知、机器人技能学习与发育等方面的研究。


具体研究工作:

1. 受脑启发的感知认知计算、类脑脉冲神经网络等研究;

  该研究旨在深入探索大脑感知认知机制,构建类脑计算模型,并将其应用于人工智能系统中。通过模仿大脑运行机制、神经元间的信息传递和处理方式,实现高效的类脑感知和学习能力,并致力于将其进行机器人、无人系统的部署实现。

2. 面向机器人、无人机的视觉检测、识别与跟踪算法研究和应用;

  该研究方向专注于开发机器人和无人机的视觉系统,以实现环境感知和目标处理能力。研究内容涵盖基于深度学习的目标检测与识别算法、视觉跟踪算法的改进与优化,克服在实际场景中光照、雨雾、遮挡、距离等造成的影响。开展机器人在复杂环境中的自主导航、无人机的目标搜索与跟踪,以及基于视觉信息的智能决策与执行。

3. 基于强化学习、持续学习的机器人具身智能研究;

  该研究致力于实现机器人的具身智能,使其能够通过与环境的交互不断学习和适应环境完成任务。研究内容包括基于强化学习和持续学习的算法设计与优化、机器人智能操作与技能学习,以及机器人在复杂环境中的行为建模与决策控制等。


要求:

计算机、自动化、软件、电子信息、通信、物联网、测控、数学等信息类相关专业;

有较强的学习能力和意愿;

有较强的编程和数学功底;


招生专业

081104--模式识别与智能系统(硕士生,中科院自动化所

085400--人工智能(非全日制硕士,国科大人工智能学院





研究领域

类脑感认知、类脑智能机器人、机器人视觉、图像识别与检测、多模态感知、机器人自主导航、农业机器人等方面的研究。

教育背景

2010-09--2015-07   韩国 高丽大学(国家公派)   博士
2006-09--2010-07   哈尔滨工业大学   学士
学历
博士研究生学历
学位

工学博士学位

工作经历

   
工作简历
2017-11~现在, 中国科学院自动化研究所, 副研究员
2015-09~2017-10,中国科学院自动化研究所, 助理研究员
社会兼职
2023-12-01-今,中国计算机学会数字农业分会, 执行委员
2023-09-29-今,中国自动化学会青工委, 委员
2021-11-01-今,中国图象图形学会专业委员会, 委员
2020-09-01-今,工业与信息化部专家信息库, 成员
2019-10-31-今,中国自动化学会专业委员会, 委员
2017-12-30-今,北京市科技专家库, 成员
2017-01-01-今,国家自然科学基金, 评议专家
2016-06-30-今,中国自动化学会, 会员
2016-06-30-今,中国计算机学会, 会员
2015-01-01-今,IEEE Robotics and Automation Society, 会员

出版信息

[1]Y. F. Lu, J. W. Gao,  et.al, “A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios,” IEEE Transactions on Intelligent Transportation Systems, 2023. 24(7): 6989-6999.

[2]Y. F. Lu, X. Yang, et.al, “A Novel Biologically-inspired Structural Model for Feature and Correspondence,” IEEE Transactions on Cognitive and Developmental Systems, 2023. 15(2): 844-854.

[3]Y. F. Lu, Q. Yu, et.al, “Cross Stage Partial Connections based Weighted Bi-directional Feature Pyramid and Enhanced Spatial Transformation Network for Robust Object Detection,” Neurocomputing, 2022. 513: 70-82.

[4]Y. F. Lu, W. J. Zhao, “What will the robots be like in the future?” National Science Review, 2019. 6(5): 1059–1061. 

[5]J.Y. Qu, Y. F. Lu*, et.al, “Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection," IEEE Transactions on Neural Networks and Learning Systems, 2024. DOI: 10.1109/TNNLS.2024.3372613.

[6]Z.Y. Gao, Y. F. Lu*, Shengbo Eben Li*et.al, Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model," IEEE Transactions on Intelligent Transportation Systems, 2024, DOI: 10.1109/TITS.2024.3400227.

[7]L.Y. Guo, Y. F. Lu*, et.al. Transformer-based Spiking Neural Networks for Multimodal Audio-Visual Classification," IEEE Transactions on Cognitive and Developmental Systems, 2023. DOI: 10.1109/TCDS.2023.3327081.

[8]Y. Li, Y. F. Lu*, et.al. Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model," Energy Reports, 2022, 13(8): 807-814.

[9]C. Ma, Y. F. Lu*, “Distributed Nonsynchronous Event-triggered State Estimation of Genetic Regulatory Networks with Hidden Markovian Jumping Parameters”, Mathematical Biosciences and Engineering, 2022, 19(12): 13878-13910. 

[10] Y. Li, Y. F. Lu*, et.al. Electromagnetic Force Analysis of a Power Transformer under the Short-Circuit Condition," IEEE Transactions on Applied Superconductivity, 2021, 31(8): 1-3.

[11]Y. F. Lu, H. Z. Zhang, et.al, “Dominant Orientation Patch Matching for HMAX,” Neurocomputing, 2016. 193:155-166. 

[12]Y. F. Lu, T. Kang, et.al. “Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions,” Computer Vision, IET, 2015, 9(5): 663-672.

[13]Y. F. Lu, H. Qiao, et.al, “Image Recommendation based on a Novel Biologically Inspired Hierarchical Model,” Multimedia Tools and Applications, 2018, 77 (4):4323-4337.

[14]Y. F. Lu, L. H. Jia, et.al, “Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment,” International Journal on Wavelet, Multiresolution, and Information Processing,2019,17(2), 1940007.

[15]Y. F. Lu, M. Lim, et.al. “Extended Biologically Inspired Model for Object Recognition Based on Oriented Gaussian-Hermite Moment,” Neurocomputing, 2014. 139(2): 189-201. 

[16]H. Z. Zhang, Y. F. Lu, et.al, “B-HMAX: A fast Binary Biologically Inspired Model for Object Recognition,” Neurocomputing. 2016. 218: 242-250.

[17]J. Wang, Y. F. Lu*, et al. A novel CNN model for fine-grained classification with large spatial variants, International Conference on Intelligent Computing and Signal Processing, 2020.

[18]Y. F. Lu, H. Qiao, et.al, “A Novel Biologically Inspired Hierarchical Model for Image Recommendation,” 14th International Symposium on Neural Networks, Sapporo, Japan, 2017.

[19]Y. F. Lu, H. Z. Zhang, et.al. “A Novel Patch Selection Method in Salient Regions of Object recognition,” 30th Korean Conference of Institute of Control, Robotics and Systems, Seoul, South Korea, 2015.4.22-4.25.

[20]Y. F. Lu, A. X. Zhang, et.al. “Multi-Scale Scene Text Detection Based on Convolutional Neural Network,” Chinese Automation Congress, 2019. DOI:10.1109/CAC48633.2019.8996635.

[21]Y. F. Lu, H. Z. Zhang, et.al. Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint. Institute of Control, Robotics and Systems, 2015:53-54.

[22]W. Y. Zhang, Y. F. Lu*, et.al. “Convolutional Neural Networks on Apache Storm,” Chinese Automation Congress, 2019.DOI:10.1109/CAC48633.2019.8996300.

[23]B. C. Liu, Y. F. Lu*, et al. Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot, Chinese Automation Congress, 2021. DOI: 10.1109/CAC53003.2021.9727350.

[24]J. Y. Chang, Y. F. Lu*, et al. Long-distance tiny face detection based on enhanced YOLOv3 for unmanned systemInternational Conference on Intelligent Unmanned Systems, 2020. 

[25]Y. Li, Y. F. Lu*, Dynamic Electromagnetic Force Analysis of a Power Transformer with Regulated Windings, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2020.

[26]Z. D. Sun, Y. F. Lu*, Underwater attached organisms intelligent detection based on an enhanced YOLO, IEEE International Conference on Electrical Engineering, Big Data and Algorithms, 2022, 1118-1122.

[27]Z. Y. Li, Y. F. Lu*, et al. Memory Mechanisms Based Few-shot Continual Learning Railway Obstacle Detection, The 5th China Symposium on Cognitive Computing and Hybrid Intelligence, 2023.

[28]Z. Yang, Y. F. Lu*, et al. "Enhanced U-Net++ for brain tumor segmentation based on data enhancement", Proc. SPIE 12724, International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241U, DOI:10.1117/12.2687856.

[29]H. Luo, Y. F. Lu*, et al. DeepLabV3-SAM: A Novel Image Segmentation Method for Rail Transportation, 2023 3rd International Conference on Electronic Information Engineering and Computer Communication, 2023.

[30]D. Y. Lee, Y. F. Lu, et.al. "3-D Vision Based Local Obstacle Avoidance Method for Humanoid Robot," 2012 International Conference on Controls Automation and Systems, JejuSouth Korea, 2012.10.20-10.23.

[31]商迪,吕彦锋*,乔红,受人脑中记忆机制启发的增量目标检测方法,计算机科学,2023, 50 (2): 267-274. 


科研活动

   
科研项目
( 1 ) 基于视皮层腹侧与背侧通路融合计算模型的动态场景下跟踪研究, 负责人, 国家任务, 2017-01--2019-12
( 2 ) 基于数据学习的无人机************, 负责人, 国家任务, 2018-01--2020-12
( 3 ) 基于类脑视觉模型的动态场景下车辆快速追踪研究, 负责人, 中国科学院计划, 2019-01--2020-12
( 4 ) 无人机与有人机共融博弈的基础理论与关键技术研究, 参与, 国家任务, 2017-01--2020-12
( 5 ) 面向机器人自主操作的人体上肢行为识别-模仿学习研究, 参与, 国家任务, 2018-01--2020-12
( 6 ) 基于混合双目视觉的移动机器人环境主动感知, 参与, 国家任务, 2018-01--2020-12
( 7 ) 基于类脑学习的非结构化环境感知方法, 参与, 国家任务, 2017-01--2019-06
( 8 ) 重载卡车客车用液态模锻高强韧轻量化车轮智能制造新模式, 参与, 国家任务, 2017-05--2020-06
( 9 ) 新一代智能网联汽车视觉和环境感知处理系统, 参与, 地方任务, 2018-07--2021-07
( 10 ) 基于水下机器人的船体附着物视觉感知技术研究, 负责人, 国家任务, 2020-01--2021-12
( 11 ) 移动机器人认知技术研究, 参与, 地方任务, 2016-01--2018-12
( 12 ) 柔性物体检测和识别仿真软件系统开发, 负责人, 地方任务, 2017-05--2018-12
( 13 ) 面向轮式机器人的多模态脉冲神经网络驱动下的目标识别, 负责人, 国家任务, 2020-11--2023-10
( 14 ) 基于移动机器人的多目标视觉检测与追踪研究, 负责人, 地方任务, 2020-12--2021-12
( 15 ) 基于受脑启发的小样本连续任务学习的轨道列车障碍物识别方法研究, 负责人, 地方任务, 2021-12--2024-12
( 16 ) 序列红外图像检测跟踪技术开发项目, 负责人, 境内委托项目, 2023-01--2025-01
( 17 ) 大田多功能田间管理作业机器人研发, 负责人, 国家任务, 2023-10--2026-12
( 18 ) 复杂地形下的稳定行走无人车底盘模块构建, 负责人, 中国科学院计划, 2024-01--2028-12
参与会议
(1)Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot   2022-08-11
(2)Insulator Defect Detection for Power Grid Based on Light Correction Enhancement and YOLOv5 Model   2022-06-08
(3)A novel CNN model for fine-grained classification with large spatial variants   2020-04-21
(4)Human Motion Prediction based on Visual Tracking   2019-11-24
(5)Multi-Scale Scene Text Detection Based on Convolutional Neural Network   2019-11-22
(6)Convolutional Neural Networks on Apache Storm   2019-11-22
(7)A Novel Biologically Inspired Hierarchical Model for Image Recommendation   2017-07-22
(8)A fast Binary Biologically Inspired Model for Object Recognition   2015-04-25
(9)A Novel Patch Selection Method in Salient Regions of Object recognition   2015-04-22
(10)3-D Vision Based Local Obstacle Avoidance Method for Humanoid Robot   2012-10-20