Gang Li Professor

Li Gang, male, professor, postdoctoral fellow, School of Energy and Electricity - international doctoral/master supervisor. He has successively obtained bachelor, master and doctor degrees in computer and application major of Chang'an University, traffic information engineering and control major of Chang'an University, and worked in the civil engineering postdoctoral mobile station of Chang'an University from 2011 to 2013; from 2016 to 2017 in Tulane University in the United States Work as a visiting scholar.  He has long-term research accumulation in artificial intelligence algorithm integration electrical engineering, transportation energy integration, smart transportation, machine learning, intelligent detection and software engineering, and has successively presided over the research on key industrial chains of Shaanxi Science and Technology Department, provincial natural science basic research projects, Guangxi More than 20 projects including key research and development plans of the Science and Technology Department, Xi'an Science and Technology Plan projects, and horizontal cooperative development projects, and 3 software copyrights. In Automation in Construction, Measurement, Measurement Science and Technology, Structural Control and Health Monitoring, Computer Methods and Programs in Biomedicine, International Journal of Pavement Engineering, Engineering Computations, Journal of Infrastructure Systems, Computer Methods and Programs in Biomed icine, Journal of Highway Studies , Journal of Transportation Engineering, Computer Science, Progress in Laser and Optoelectronics, Acta Photonics, Journal of Engineering Graphics, Computer Engineering and Applications, Microelectronics and Computers, etc. Published more than 50 papers in important academic journals. He has lectured more than 10 courses for undergraduates and postgraduates, and is currently guiding 16 postgraduates.  research direction  Artificial intelligence integrates electrical engineering, transportation energy integration, intelligent transportation, machine learning, intelligent detection and software engineering.  Contact information  Postgraduate application contact:  Phone/WeChat: 15229296166  Email1: 15229296166@chd.edu.cn  Every year, 5-6 graduate students are recruited (including electrical engineering master's degree, master's degree, and energy and power master's degree). Welcome to apply.

In recent years, the academic papers published in the fields of intelligent transportation, machine vision and new energy integration are as follows：

[1]           邓有为, 李刚*, 辛怡, 等. 隧道围岩沉降自适应实时识别及测量[J]. 电子测量与仪器学报，2023, 37(2): 236.通讯作者

[2]           李雯雯, 李喜媛, 周健, 谢耀华, 李刚*. 基于全卷积神经网络和朴素贝叶斯数据融合的桥梁裂缝识别算法[J].公路交通科技，2023,40(2):44-52 https://doi.org/10.3969/j.issn.1002-0268.2023.02.007

[3]           何廷全，俞山川，张生鹏，兰栋超，李刚*，基于目标检测的驾驶人分神驾驶行为检测方法研究[J].公路交通科技，2022,39(10):153-161

[4]           Shanchuan Yu, Yi Li, Zhaoze Xuan, Yishun Li, and Gang Li*，Real-Time Risk Assessment for Road Transportation of Hazardous Materials Based on GRU-DNN with Multimodal Feature Embedding[J]. Applied Sciences,2022,12(21):11130; https://doi.org/10.3390/app122111130

[5]           Gang Li, Zhongyuan Fang, AL MAHBASHI, MOHAMMED, Tong Liu, Zhihao Deng, Automated bridge crack detection based on improving encoder-decoder network and stripe pooling[J]. Journal of Infrastructure Systems, 2023, 29(2): 04023004.

[6]           Gang Li*, Tong Liu, Zhongyuan Fang, Qian Shen, Jawad Ali, Automatic bridge crack detection using boundary refinement based on real-time segmentation network[J]. Structural Control and Health Monitoring，2022:e2991.https://doi.org/10.1002/stc.2991

[7]           QiHong Li, LingJia Liu, YongJun Zhou, Gang Li*, and Yu Zhao, Robust, accurate, and improved measurement of structural deformation based on off-axis digital image correlation[J].Applied Optics, 2022,61(1):1616-1623

[8]           Xuan Zheng, Shuailong Zhang, Xue Li, Gang Li*, Lightweight Bridge Crack Detection Method Based on SegNet and Bottleneck Depth-Separable Convolution with Residuals[J]. IEEE Access, 2021,9(12):161649-161668

[9]           谢耀华，代玉，周欣，李刚*，基于双向特征金字塔和残差网络的危化品运输车辆检测，2022,31(1):218-225

[10]        李刚，陈永强，何廷全等，改进的多分支特征共享结构网络在路面裂缝检测中的应用[J].激光与光电子学进展，2022，59(12):1215005

[11]        Gang Li, Yongqiang Chen, Jian Zhou, et al. Road crack detection and quantification based on segmentation network using architecture of matrix[J]. Engineering Computations, 2021,6:

[12]        Gang Li, Dongchao Lan, et al. Automatic pavement crack detection based on single stage salient-instance segmentation and concatenated feature pyramid network[J]. International Journal of Pavement Engineering, 2021,6:

[13]        Gang Li, Xiyuan Li, Jian Zhou, et al. Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network[J]. Measurement, 2021,176(5):109171.

[14]        Wenting Qiao, Qiangwei Liu, Xiaoguang Wu, Biao Ma, Gang Li*. Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module[J]. Sensors, 2021,21(9), 2902. 通讯作者

[15]        Wenting Qiao, Biao Ma, Qiangwei Liu, Xiaoguang Wu, Gang Li*. Computer Vision-based Bridge Damage Detection using Deep Convolutional Networks with Expectation Maximum Attention Module[J]. Sensors, 2021, 21(3), 824. 通讯作者

[16]        Gang Li, Qiangwei Liu, Wei Ren, et al. Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field[J]. Measurement, 2021,170(1):108693

[17]        Gang Li, Xueli Ren, Wenting Qiao, et al.Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing[J]. Structural Control and Health Monitoring, 2020,27(11):1-20. SCI检索

[18]        Gang Li, Jian Wan, Shuanhai He, et al. Semi-Supervised Semantic Segmentation Using Adversarial Learning for Pavement Crack Detection[J]. IEEE Access,2020, 8(3):51446-51459.

[19]        Gang Li, Biao Ma, Shuanhai He, et al.Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique[J]. Sensors,2020,20(3):1-23.

[20]        Gang Li, Qiangwei Liu, et al. Automatic crack recognition for concrete bridges by fully convolutional neural network and Naive Bayes data fusion based on visual detection system[J]. Measurement Science and Technology, 2020, 27(4):1-17. SCI二区检索：000532330500001

[21]        Gang Li, Chao Wang, Depeng Han, et al.Deep Principal Correlated Auto-Encoders with Application to Imaging and Genomics Data Integration[J]. IEEE Access, 2020,8(1):20093 – 20107.

[22]        Gang Li, Depeng Han, Chao Wang, et al. Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia[J]. Computer Methods and Programs in Biomedicine, 2020, 183(1): 1-9.SCI一区检索：000498062700005

[23]        李刚，高振阳等，改进的全局卷积网络在路面裂缝检测中的应用研究[J].激光与光电子学进展，2020，57（8）:081011

[24]        李刚，刘强伟等，复杂背景下交错低秩组卷积混合深度网络的路面裂缝检测算法研究[J]，激光与光电子学进展，2020，57(14):141031-141038

[25]        李刚，张宇博等，改进的卷积网络目标跟踪算法[J]. 计算机应用研究，2020，37（7）:2206-2210

[26]        李刚，韩德鹏，刘强伟等，基于典型相关稀疏自编码器的精神分裂症分类研究[J]. 中国医学物理学杂志，2020,3(3):391-396.

[27]        李刚，王超，韩德鹏等，基于深度主成分相关自编码器的多模态影像遗传数据研究[J]. 计算机科学，2020,4(4):60-66.

[28]        Gang Li, Xiaoxing Zhao, Kai Du, et al. Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine[J]. Automation in Construction, 2017, 78(6):51-61.（SCI一区检索：000397353900005）

[29]        Gang Li, Shuanhai He, Yongfeng Ju, et al. Long-distance precision inspection method for bridge cracks with image processing[J]. Automation in Construction,2014,41(5):89-95.（SCI一区检索：000334139500010）

[30]        李刚，贺拴海，巨永锋等，远距离混凝土桥梁结构表面裂缝精确提取算法[J].中国公路学报，2013,4(7)：1-8.（EI检索:20133516679465）

[31]        李刚，贺拴海，杜凯等. 桥梁下部结构裂缝提取的改进C-V模型算法[J]. 交通运输工程学报，2012，12(4)：9-16.（EI检索:20124315604048）

[32]        Li Gang, He ShuanHai, Ju YongFeng, et al.. Image-Based Method for Concrete Bridge Crack Detection[J].Journal of Information & Computational Science,2013,10(8):2229-2236.（EI检索:2013251642907）

[33]        Li Gang,Ju Yongfeng.Novel Approach to Pavement Cracking Detection Based on Morphology and Mutual Information[C], The 2010 Chinese Control and Decision Conference. Xuzhou,China,2010: 3219-3223.(EI检索:20103213139942)

[34]        Gang Li.Improved Pavement Distress Detection Based on Contourlet Transform and Multi-direction Morphological Structuring Elements[C].Advanced Materials Research,2012,466-467:371-375,(EI检索:20121114858887)

[35]        李刚，贺昱曜，不均匀光照的路面裂缝识别和分类新方法[J].光子学报，2010，39 (8): 1405-1408

[36]        李刚，贺昱曜，赵妍，一种改进的多方位结构元素形态学和互信息量的图像分割算法[J].工程图学学报，2010，31(3):104-108

[37]        李刚，贺昱曜，多方位结构元素路面裂缝图像边缘识别算法[J].计算机工程与应用，2010，46(1)：224-226

[38]        李刚，贺昱曜，赵妍，基于大津法和互信息量的路面破损图像自动识别算法[J].微电子学与计算机，2009，26(7)：241-243

[39]        李刚，贺昱曜，赵妍，桥梁水下结构缺陷的图像分割与特征提取算法[J].计算机应用与软件，2010，27(4)：80-82

[40]       Li Gang, He ShuanHai, Ju YongFeng, Concrete Cracks Recognition Based on C-V Model and Mutual Information[J]. International Journal of Advancements in Computing Technology,2012,5(6): 415-423