基于图像处理的轨旁信号机识别方法

doi:10.16180/j.cnki.issn1007-7820.2022.08.009

摘要/Abstract

摘要：

信号机是用于指挥列车运行的重要信号设备。为了进一步保障行车安全,文中提出了一种基于图像处理的准确定位轨旁信号机并识别其颜色信号的方法。该方法根据先验值提取ROI,对ROI图像在RGB色彩空间进行颜色分割以剔除无关背景干扰,同时利用形态学处理降噪。对处理后的图像进行霍夫圆变换来提取信号机的候选圆,并根据信号机位于轨道右侧的位置特点定位出指挥当前轨道列车运行的信号机,然后通过分析定位出的信号机区域内的像素值信息来识别其颜色信号。实验结果表明,该方法能精确定位和识别轨旁信号机,对单一红、黄、绿3种颜色的信号识别率分别为91.42%、85.00%和94.29%。

关键词: 轨旁信号机, 图像处理, 颜色分割, 形态学处理, 边缘检测, 轨道定位, 霍夫变换, 信号机识别

Abstract:

The trackside signal light is one of the important components for prompt train operation. In order to ensure train operation safety, a method based on image processing technique is proposed to effectively locate the trackside signal light and identify its color information. The trackside signal light ROI is extracted through the empirical value, and then the color segmentation is carried out to the ROI in the RGB color space to avoid the influence of irrelevant background, and remaining noise is removed through morphology processing. Hough-circle transform is performed on the processed image for the extracted candidate region of signal light, and the operating trackside signal light related to the running train is located according to the position characteristics between the signal light and the track. The pixel value information in the signal light area is analyzed for signal color recognition. The experimental results indicate that the method can precisely locate and recognize trackside signals, and the color correction ratio is 91.42% for red, 85.00% for yellow, and 94.29% for green, respectively.

Key words: trackside signal light, image processing, morphology processing, color segmentation, edge detection, rail location, Hough transform, signal light recognition

中图分类号:

TP391

冯俊逸,沈拓,张轩雄. 基于图像处理的轨旁信号机识别方法[J]. 电子科技, 2022, 35(8): 53-57.

FENG Junyi,SHEN Tuo,ZHANG Xuanxiong. Trackside Signal Light Recognition Based on Image Processing[J]. Electronic Science and Technology, 2022, 35(8): 53-57.

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参考文献 18

[1]	刘伯鸿, 李国宁. 城市轨道交通信号[M]. 成都: 西南交通大学出版社, 2011.
	Liu Bohong, Li Guoning. Urban rail transit signal[M]. Chengdu: Southwest Jiaotong University Press, 2011.
[2]	李惠斌. 机车信号常见故障的分析及处理措施[J]. 中国新技术新产品, 2019(15):30-31.
	Li Huibin. Analysis and treatment of cab signal common faults[J]. New Technology & New Products of China, 2019(15):30-31.
[3]	杨智杰. 基于RGB彩色通道的结构化道路车道线检测[J]. 电子科技, 2015, 28(1):95-98.
	Yang Zhijie. Lane detection based on RGB channels[J]. Electronics Science and Technology, 2015, 28(1):95-98.
[4]	Wang W H, Sun S, Jiang M, et al. Traffic lights detection and recognition based on multi-feature fusion[J]. Multimedia Tools and Applications, 2017, 76(13):14829-14846. doi: 10.1007/s11042-016-4051-5
[5]	Lee S H, Kim J H, Lim Y J, et al. Traffic light detection and recognition based on haar-like features[C]. Honolulu:Proceedings of International Conference on Electronics, Information, and Communication, 2018.
[6]	Omachi M, Omachi S. Traffic light detection with color and edge information[C]. Beijing:Proceedings of the Second IEEE International Conference on Computer Science and Information Technology, 2009.
[7]	Wang C X, Jin T, Yang M, et al. Robust and real-time traffic lights recognition in complex urban environments[J]. International Journal of Computational Intelligence Systems, 2011, 4(6):1383-1390.
[8]	Li J, Dong Y. A new night traffic light recognition method[C]. Shanghai:Proceedings of the International Seminar on Computer Science and Engineering Technology, 2019.
[9]	张坤, 李珊珊, 王晓红, 等. 基于机器视觉的铁路信号灯检测技术[J]. 河北工业科技, 2019, 36(2):115-121.
	Zhang Kun, Li Shanshan, Wang Xiaohong, et al. Detection technology of railway signal lamp based on machine vision[J]. Hebei Journal of Industrial Science and Technology, 2019, 36(2):115-121.
[10]	Gorobetz M, Alps I, Beinarovica A, et al. Algorithm of signal recognition for railway embedded control devices[C]. Riga:Proceedings of IEEE the Fifty-ninth International Scientific Conference on Power and Electrical Engineering of Riga Technical University, 2018.
[11]	Gonzalez R C, Woods R E. Digital image processing, third edition[J]. Journal of Biomedical Optics, 2009, 14(2):1-2.
[12]	Illingworth J, Kittler J. The adaptive hough transform[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5):690-698. pmid: 21869428
[13]	巨志勇, 张文馨, 翟春宇. 基于改进Canny算子的垃圾图像边缘检测[J]. 电子科技, 2020, 33(8):16-20.
	Ju Zhiyong, Zhang Wenxin, Zhai Chunyu. Garbage image edge detection based on improved Canny algorithm[J]. Electronics Science and Technology, 2020, 33(8):16-20.
[14]	杨婷婷, 顾梅花, 章为川, 等. 彩色图像边缘检测研究综述[J]. 计算机应用研究, 2015, 32(9):2566-2571.
	Yang Tingting, Gu Meihua, Zhang Weichuan, et al. Review of color image edge detection algorithms[J]. Application Research of Computers, 2015, 32(9):2566-2571.
[15]	毛星云, 冷雪飞. OpenCV3编程入门[M]. 北京: 电子工业出版社, 2015.
	Mao Xingyun, Leng Xuefei. OpenCV3 programming primer[M]. Beijing: Publishing House of Electronics Industry, 2015.
[16]	Zhang Y, Xue J, Zhang G, et al. A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles[C]. Nanjing:Proceedings of the Thirty-first Chinese Control Conference, 2014.
[17]	金充充, 何立平, 朱仲杰, 等. 雾霾天气下交通信号灯定位与识别算法[J]. 浙江万里学院学报, 2018, 31(6):61-68.
	Jing Chongchong, He Liping, Zhu Zhongjie, et al. Traffic light location and recognition algorithm in haze weather[J]. Journal of Zhejiang Wanli University, 2018, 31(6):61-68.
[18]	王子冠, 殳国华. 基于传统图像处理算法和深度学习的轨道区域识别研究[J]. 电气自动化, 2019, 41(4):111-114.
	Wang Ziguan, Shu Guohua. Research on track section identification based on traditional image processing algorithm and deep learning[J]. Electrical Automation, 2019, 41(4):111-114.

	R	G	B
红灯	[200,255]	[0,100]	[0,90]
绿灯	[0,50]	[200,255]	[100,180]
黄灯	[200,255]	[200,255]	[50,110]