
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
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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。主要从事机器人多模态感知、类脑认知增强、具身智能、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五十余篇,主持了科技创新2030“新一代人工智能”、国家重大科技GG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、中国计算机学会数字农业分会执行委员。担任SCI 期刊 Electronics 客座编辑, IEEE TNNLS、IEEE TITS、IEEE TCDS、PR、IROS等国际期刊会议审稿人。
主要从事:机器人多模态感知、类脑认知增强、具身智能、计算机视觉、农业机器人等方面的研究。
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、机器人多模态感知、具身智能、图像检测与跟踪、农业机器人等方面的研究。
教育背景
学历
学位
工学博士学位
工作经历
工作简历
社会兼职
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
专利与奖励
[1] 大模型图像分割方法、装置、设备、存储介质及程序产品, 发明专利, 2024, 第一作者, CN119107461A
[2] 基于脉冲神经网络的视觉追踪方法、装置及电子设备, 发明专利, 2024, 第一作者, CN118570249A
[3] 基于脉冲神经网络的模型优化方法、装置及电子设备, 发明专利, 2024, 第一作者, CN118485124A
[4] 基于小样本持续学习模型的轨道交通障碍物识别方法, 发明专利, 2024, 第一作者, CN118334432A
[5] 监控场景下的异常闯入物实时检测方法及装置, 发明专利, 2021, 第一作者, CN113837001A
[6] 一种基于离散元柔性杂草模型的除草效果定量分析方法, 发明专利, 2025, 第二作者, CN119323164A
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]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, 2025. 36(3): 4934-4946.
[4]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. 25(10): 13067-13079.
[5]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.
[6] J.Y. Qu, Y. F. Lu*, et.al, Spike-based high energy efficiency and accuracy tracker for Robot, 2024 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2024). (Best Paper Award on Cognitive Robotics -Finalists)
[7] Z.Y. Li, Y. F. Lu*, et.al, Vision-Language Navigation with Continual Learning for Unseen Environments, 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025).
[8]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.
[9]Y. F. Lu, W. J. Zhao, “What will the robots be like in the future?” National Science Review, 2019. 6(5): 1059–1061.
[10] Y.K. Wang, Y. F. Lu*, LiSegAgr: Labeled Instance Segmentation for Agricultural Remote Sensing Images through Iterative SAM, International Conference on Neural Information Processing (ICONIP 2024).
[11] S. Yang, T. Zhan, Y. F. Lu*,J. Wang* et al. “ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving”, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2025)
[12] Y. Li, Y. F. Lu*, et.al, "Intelligent Inspection System for Power Insulators based on AAV on Complex Weather Conditions." IEEE Transactions on Applied Superconductivity (2024).
[13] Y. Li, Y. F. Lu*, et.al, "Design of a Wireless Power Transmission System with Magnetically Integrated Compensation Network." IEEE Transactions on Applied Superconductivity (2024).
[14]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.
[15]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.
[16] 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.
[17]Y. F. Lu, H. Z. Zhang, et.al, “Dominant Orientation Patch Matching for HMAX,” Neurocomputing, 2016. 193:155-166.
[18]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.
[19]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.
[20]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.
[21]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.
[22]H. Z. Zhang, Y. F. Lu, et.al, “B-HMAX: A fast Binary Biologically Inspired Model for Object Recognition,” Neurocomputing. 2016. 218: 242-250.
[23]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.
[24]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.
[25]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.
[26]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.
[27]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.
[28] Z.Y. Li, Y. F. Lu*, et.al. Brain-Inspired Visual Language Navigation Robot Position Deviation Correction. In International Conference on Intelligent Robotics and Applications (ICIRA 2024).
[29]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.
[30]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.
[31]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.
[32]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.
[33]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.
[34]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.
[35]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
SCI 《Electronics》 CALL FOR PAPER. 欢迎投稿!
Special Issue: Object Detection in Autonomous Driving
https://www.mdpi.com/journal/electronics/special_issues/03I51OVVX9
Deadline for manuscript submissions: 15 April 2025