联系方式

通讯地址：福建省福州市福州地区大学新区学园路2号 邮编:350116

电子邮箱： liqiongchen@fzu.edu.cn或312676764@qq.com

教育工作经历

2022/01-至今，福州大学，机械工程及自动化学院/先进技术创新研究院/智能光电教育部重点实验室，校聘副研究员

2021/07-2021/12，福州大学，机械工程及自动化学院，讲师

2016/09-2021/06，武汉大学，电子信息学院，电路与系统，博士

2012/09-2016/06，武汉大学，电子信息学院，电波传播与天线，学士

研究方向

1）计算机视觉（红外小目标检测、目标跟踪、超分辨率重建等）

2）遥感图像智能解译（舰船检测、舰船目标跟踪、舰船细粒度识别）

3）人工智能算法应用研究

代表性论著

1. 学术论文

[1]Chen L, Yang X, Wang S, et al. PFAN:Progressive Feature Aggregation Network for Lightweight Image Super-resolution[J]. The Visual Computer, 2025.

[2] Ni R, Wu J,Qiu Z*,Chen L*, et al., Point-to-Point Regression: Accurate Infrared Small Target Detection with Single-Point Annotation[J], IEEE Transactions on Geoscience and Remote Sensing, 2025, 63:1-19.

[3] Shen Y, Xie X, Wu J,Chen L*, Huang F*. EAFF-Net: Efficient Attention Feature Fusion Network for Dual-modality Pedestrian Detection[J], Infrared Physics and Technology, 2025, 145:105696.

[4]黄峰,刘鸿伟,沈英,裘兆炳,陈丽琼*.基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法[J].模式识别与人工智能, 2025, 38(1): 36-50.

[5]Chen L, Wu T, Zheng S, et al. Robust Unsupervised Multifeature Representation for Infrared Small Target Detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17:10306-10323.

[6] Wu J, Ni R, Chen Z, Huang F,Chen L*. FEFN: Feature Enhancement Feedforward Network for Lightweight Object Detection in Remote Sensing Images[J]. Remote Sensing, 2024, 16, 2398.

[7] Wang S, Zeng D, Xu Y, Yang G, Huang F*,Chen L*, Towards Complex Scenes: A Deep Learning-based Camouflaged People Detection Method for Snapshot Multispectral Images[J]. Defence Technology, 2024, 34:269-281.

[8] Shen Y, Zheng W, Huang F*, Wu J,Chen L*. Reparameterizable Multibranch Bottleneck Network for Lightweight Image Super-Resolution[J]. Sensors, 2023,23(8): 3963.

[9] Shen Y, Zheng W,Chen L, Huang F*. RSHAN: Image Super-resolution Network based on Residual Separation Hybrid Attention Module[J]. Engineering Applications of Artificial Intelligence, 2023, 122:106072.

[10]Chen L, Lin L. Improved Fuzzy C-Means for Infrared Small Target Detection[J]. IEEE geoscience and remote sensing letters, 2022(19): 1-5.

[11]吴靖,叶晓晶,黄峰*,陈丽琼,王志锋,刘文犀.基于深度学习的单帧图像超分辨率重建综述[J].电子学报, 2022, 50(9): 2265-2294.

[12] Cheng M, Fan C*,Chen L, Zou L, Wang J, Liu Y, Yu H. Partial Atrous Cascade R-CNN[J]. Electronics, 2022, 11(8):1241.

[13]Chen L, Shi W, Deng D. Improved YOLOv3 based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images[J]. Remote Sensing, 2021, 13(4):660.

[14] Qiu Z, Lin L,Chen L. An Active Method to Improve the Measurement Accuracy of Four-Quadrant Detector[J]. Optics and Lasers in Engineering, 2021, 146:106718.

[15] Shen Y, Li J, Lin W,Chen L, Huang F, Wang S*. Camouflaged Target Detection based on Snapshot Multispectral Imaging[J]. Remote sensing, 2021, 13(19):3949.

[16] Huang F, Wang Z, Wu J, Shen Y*,Chen L. Residual Triplet Attention Network for Single-Image Super-Resolution[J]. Electronics, 2021, 10(17):2072.

[17]Chen L, Shi W, Fan C, et al. A Novel Coarse-to-fine Method of Ship Detection in Optical Remote Sensing Images based on a Deep Residual Dense Network[J]. Remote Sensing, 2020, 12(19):3115.

[18]Chen L, Zou L, Fan C, et al. Feature Weighting Network for Aircraft Engine Defect Detection, International Journal of Wavelets Multiresolution and Information Processing[J], 2020, 18(3):1-12.

[19] Nie P, Fan C, Zou L,Chen L, Li X. Crowd Counting Guided by Attention Network[J]. Information 2020, 11, 567.

[20]陈丽琼,石文轩,范赐恩,等.基于多分类学习的光学遥感图像舰船检测[J].华中科技大学学报(自然科学版)，2019，47(5):62-67.

2. 国家发明专利

[1]陈丽琼，范赐恩，田胜，等.基于深度残差密集网络的光学遥感图像舰船检测[P]，中国，ZL201811571859.2，已授权.

[2]陈丽琼，裘兆炳，范赐恩，等.一种基于机器视觉的铝铝泡罩包装药片缺陷检测方法[P]，中国，ZL201910174967.4，已授权.

[3]陈丽琼，邹炼，范赐恩，等.基于目标语义和注意力机制的图像场景分类方法及装置[P]，中国，ZL201911311047.9，已授权.

[4]陈丽琼，田胜，邹炼，等.基于深度学习的跨摄像头行人检测跟踪方法[P]，中国，ZL201810512107.2，已授权.

3.软件著作权

[1]场景图片智能定位软件，登记号：2020SR1901464.

[2]铝塑泡罩包装胶囊缺陷检测软件，登记号：2020SR1897053.

[3]无人机航拍图像目标检测系统，登记号：2023SR1672272.

[4]深度学习图像超分辨率重建系统，登记号：2023SR1672306.

[5]伪装目标检测系统，登记号：2024SR1122692.

[6]云层干扰下的舰船目标跟踪系统，登记号：2024SR1128102.

指导学生情况

2024年指导研究生（吴*）获国家奖学金；

招生寄语

课题组具有一流的科研实验平台和浓厚的学习氛围，师资力量雄厚，研究方向涉及计算机视觉、先进光学、人工智能系统等多学科交叉融合领域。往届学生就业去向包括但不限于：航天科工集团、商汤科技、科大讯飞、海康威视、星网锐捷、比亚迪、宁德时代等。

诚挚欢迎积极主动、热爱学习、热爱生活、具有团队协作精神的同学加入我们共同进步。具备编程、图像处理、深度学习等相关科研经验者优先。