[1] 罗超, 黄成洋. 无砟轨道底座板混凝土裂缝的研究[J]. 工程建设与设计, 2019(10):192-193. Luo C, Huang C Y. Research on concrete cracks in the base slab of ballastless track[J]. Engineering Construction and Design, 2019(10):192-193. (in Chinese)
[2] Fan D P, Ji G P, Sun G, et al. Camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:2777-2787.
[3] 王登涛, 李再帏, 何越磊, 等. 基于热成像的高速铁路轨道板表面裂缝检测方法研究[J]. 铁道标准设计, 2020, 64(7):22-28. Wang D T, Li Z W, He Y L, et al. Research on surface crack detection method of high-speed railway track slab based on thermal imaging[J]. Railway Standard Design, 2020, 64(7):22-28. (in Chinese)
[4] 王登涛, 路宏遥, 孟翔震, 等. 轨道板表面裂缝的热成像检测效果仿真分析[J]. 智能计算机与应用, 2020, 10(2):132-137. Wang D T, Lu H Y, Meng X Z, et al. Simulation analysis of thermal imaging detection effect of track plate surface cracks[J]. Intelligent Computers and Applications, 2020, 10(2):132-137. (in Chinese)
[5] 章梦. 基于图像处理的轨道裂缝检测技术的研究[D]. 上海:上海应用技术大学, 2019.
[6] 薛峰, 赵丽科, 柴雪松, 等. 基于图像处理的铁路轨道板裂缝检测研究[J]. 铁道建筑, 2015(12):123-126. Xue F, Zhao L K, Chai X S, et al. Study on detecting crack in railway track slab based on image processing technology[J]. Railway Engineering, 2015(12):123-126. (in Chinese)
[7] 战友, 阳恩慧, 马啸天, 等. 无砟轨道板裂缝三维激光检测系统研发与算法验证[J]. 铁道学报, 2021, 43(7):114-120. Zhan Y, Yang E H, Ma X T, et al. Development and algorithm verification of a threedimensional laser inspection system for cracks in ballastless track slabs[J]. Journal of the China Railway Society, 2021, 43(7):114-120. (in Chinese)
[8] 沈子豪. 基于机器视觉的CRTS II型轨道板裂缝检测技术研究[D]. 上海:上海应用技术大学, 2020.
[9] Fang F, Li L, Gu Y, et al. A novel hybrid approach for crack detection[J]. Pattern Recognition, 2020, 107:107-118.
[10] 李良福, 马卫飞, 李丽, 等. 基于深度学习的桥梁裂缝检测算法研究[J]. 自动化学报, 2019, 45(9):1727-1742. Li L F, Ma W F, Li L, et al. Research on bridge crack detection algorithm based on deep learning[J]. Journal of Automatica Sinica, 2019, 45(9):1727-1742. (in Chinese)
[11] 翁飘, 陆彦辉, 齐宪标, 等. 基于改进的全卷积神经网络的路面裂缝分割技术[J]. 计算机工程与应用, 2019, 55(16):235-239, 245. Weng P, Lu Y H, Qi X B, et al. Road crack segmentation technology based on improved fully convolutional neural network[J]. Computer Engineering and Applications, 2019, 55(16):235-239, 245. (in Chinese)
[12] 朱苏雅, 杜建超, 李云松, 等. 采用U-Net卷积网络的桥梁裂缝检测方法[J]. 西安电子科技大学学报, 2019, 46(4):35-42. Zhu S Y, Du J C, Li Y S, et al. Bridge crack detection method using U-Net convolutional network[J]. Journal of Xidian University, 2019, 46(4):35-42. (in Chinese)[1] 罗超, 黄成洋. 无砟轨道底座板混凝土裂缝的研究[J]. 工程建设与设计, 2019(10):192-193. Luo C, Huang C Y. Research on concrete cracks in the base slab of ballastless track[J]. Engineering Construction and Design, 2019(10):192-193. (in Chinese)
[2] Fan D P, Ji G P, Sun G, et al. Camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:2777-2787.
[3] 王登涛, 李再帏, 何越磊, 等. 基于热成像的高速铁路轨道板表面裂缝检测方法研究[J]. 铁道标准设计, 2020, 64(7):22-28. Wang D T, Li Z W, He Y L, et al. Research on surface crack detection method of high-speed railway track slab based on thermal imaging[J]. Railway Standard Design, 2020, 64(7):22-28. (in Chinese)
[4] 王登涛, 路宏遥, 孟翔震, 等. 轨道板表面裂缝的热成像检测效果仿真分析[J]. 智能计算机与应用, 2020, 10(2):132-137. Wang D T, Lu H Y, Meng X Z, et al. Simulation analysis of thermal imaging detection effect of track plate surface cracks[J]. Intelligent Computers and Applications, 2020, 10(2):132-137. (in Chinese)
[5] 章梦. 基于图像处理的轨道裂缝检测技术的研究[D]. 上海:上海应用技术大学, 2019.
[6] 薛峰, 赵丽科, 柴雪松, 等. 基于图像处理的铁路轨道板裂缝检测研究[J]. 铁道建筑, 2015(12):123-126. Xue F, Zhao L K, Chai X S, et al. Study on detecting crack in railway track slab based on image processing technology[J]. Railway Engineering, 2015(12):123-126. (in Chinese)
[7] 战友, 阳恩慧, 马啸天, 等. 无砟轨道板裂缝三维激光检测系统研发与算法验证[J]. 铁道学报, 2021, 43(7):114-120. Zhan Y, Yang E H, Ma X T, et al. Development and algorithm verification of a threedimensional laser inspection system for cracks in ballastless track slabs[J]. Journal of the China Railway Society, 2021, 43(7):114-120. (in Chinese)
[8] 沈子豪. 基于机器视觉的CRTS II型轨道板裂缝检测技术研究[D]. 上海:上海应用技术大学, 2020.
[9] Fang F, Li L, Gu Y, et al. A novel hybrid approach for crack detection[J]. Pattern Recognition, 2020, 107:107-118.
[10] 李良福, 马卫飞, 李丽, 等. 基于深度学习的桥梁裂缝检测算法研究[J]. 自动化学报, 2019, 45(9):1727-1742. Li L F, Ma W F, Li L, et al. Research on bridge crack detection algorithm based on deep learning[J]. Journal of Automatica Sinica, 2019, 45(9):1727-1742. (in Chinese)
[11] 翁飘, 陆彦辉, 齐宪标, 等. 基于改进的全卷积神经网络的路面裂缝分割技术[J]. 计算机工程与应用, 2019, 55(16):235-239, 245. Weng P, Lu Y H, Qi X B, et al. Road crack segmentation technology based on improved fully convolutional neural network[J]. Computer Engineering and Applications, 2019, 55(16):235-239, 245. (in Chinese)
[12] 朱苏雅, 杜建超, 李云松, 等. 采用U-Net卷积网络的桥梁裂缝检测方法[J]. 西安电子科技大学学报, 2019, 46(4):35-42. Zhu S Y, Du J C, Li Y S, et al. Bridge crack detection method using U-Net convolutional network[J]. Journal of Xidian University, 2019, 46(4):35-42. (in Chinese)
