CN101699273A - Auxiliary detection device and method of image processing for on-line flaw detection of rails - Google Patents
Auxiliary detection device and method of image processing for on-line flaw detection of rails Download PDFInfo
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Abstract
一种在线钢轨探伤的图像处理辅助检测装置及方法，在列车在线运行过程中，嵌入式控制系统控制光源激励控制器使两个辅助光源照射钢轨表面，工业CCD线阵摄像机通过数字线阵图像采集接口的控制采集钢轨表面的图像信息，将信息传输到嵌入式系统，嵌入式系统使用图像处理技术对采集到的钢轨图像进行处理；从处理后的钢轨图像中统计钢轨表面的损伤信息及固有结构信息；再将所获得的钢轨表面损伤信息进行分类和损伤程度计算。最后将这些损伤分类与程度信息通过通信接口传输到在线钢轨损伤探测装置，所传输的信息再与在线钢轨损伤探测装置采集信号进行信息融合，从而减少在线钢轨损伤探测装置检测钢轨内部损伤时受表面损伤的干扰程度。
An image processing auxiliary detection device and method for on-line rail flaw detection. During the online operation of the train, the embedded control system controls the light source excitation controller to make two auxiliary light sources illuminate the rail surface, and the industrial CCD line array camera collects the digital line array image The control of the interface collects the image information of the rail surface, and transmits the information to the embedded system, and the embedded system uses image processing technology to process the collected rail image; calculates the damage information and inherent structure of the rail surface from the processed rail image information; and then classify and calculate the damage degree of the obtained rail surface damage information. Finally, the damage classification and degree information is transmitted to the online rail damage detection device through the communication interface. The degree of disturbance of the damage.
Description
技术领域technical field
本发明涉及一种在线钢轨探伤的图像处理辅助检测装置及方法。The invention relates to an image processing auxiliary detection device and method for online rail flaw detection.
背景技术Background technique
钢轨的固有几何结构及表面损伤对在线钢轨探伤的检测结果影响很大，使得在线钢轨重伤探伤装置无法区分钢轨内部核伤和钢轨表面的三角坑和剥离掉块情况。钢轨的固有几何结构及表面损伤不仅对电磁原理的钢轨探伤设备有影响，对超声原理的钢轨探伤设备同样具有较大影响，因为超声探伤需要传感器与钢轨密贴，其接触表面的耦合程度直接影响探测结果。所以，必须解决的一个问题是如何去除钢轨的固有几何结构及表面损伤对在线钢轨探伤设备探测钢轨内部损伤时造成的影响。The inherent geometric structure and surface damage of the rail have a great influence on the detection results of the online rail flaw detection, making it impossible for the online rail serious flaw detection device to distinguish the internal nuclear damage of the rail from the triangular pits and peeled off pieces on the rail surface. The inherent geometric structure and surface damage of the rail not only have an impact on the rail flaw detection equipment based on the electromagnetic principle, but also have a greater impact on the rail flaw detection equipment based on the ultrasonic principle, because the ultrasonic flaw detection requires the sensor to be closely attached to the rail, and the coupling degree of the contact surface directly affects detection results. Therefore, a problem that must be solved is how to remove the influence of the inherent geometric structure and surface damage of the rail on the online rail flaw detection equipment to detect the internal damage of the rail.
传统的解决方法是：对于超声波探伤，增加超声探头与钢轨表面的耦合接触程度，加耦合剂；对于电磁方法是要求表面平整，甚至要求表面打磨。这样，不仅操作不方便，效率低，而且增加了作业成本。The traditional solution is: for ultrasonic flaw detection, increase the coupling contact degree between the ultrasonic probe and the rail surface, and add coupling agent; for the electromagnetic method, the surface is required to be smooth, and even the surface is required to be polished. In this way, not only the operation is inconvenient, the efficiency is low, but also the operation cost is increased.
发明内容Contents of the invention
本发明的目的是提供一种在线钢轨探伤的图像处理辅助检测装置和方法，以消除钢轨的固有几何结构及表面损伤对在线钢轨探伤设备探测钢轨内部损伤时造成的影响，并且提高了可操作性，提高了工作效率，降低了作业成本。The object of the present invention is to provide an image processing auxiliary detection device and method for on-line rail flaw detection, to eliminate the influence of the inherent geometric structure and surface damage of the rail on the online rail flaw detection equipment when detecting internal damage of the rail, and to improve operability , improve work efficiency and reduce operating costs.
根据本发明的一个方面，提供了一种在线钢轨探伤的图像处理辅助检测装置，所述在线钢轨探伤的图像处理辅助检测装置包括：工业CCD线阵摄像机、辅助光源、控制机箱；控制机箱中包含嵌入式系统、系统电源、通信接口、光源激励控制器、线阵图像采集接口。其中，所述工业CCD线阵摄像机与线阵图像采集接口连接；辅助光源与光源激励控制器连接；系统电源、通信接口、光源激励控制器、数字线阵图像采集接口分别与嵌入式系统连接。According to one aspect of the present invention, an image processing auxiliary detection device for on-line rail flaw detection is provided. The image processing auxiliary detection device for online rail flaw detection includes: an industrial CCD line array camera, an auxiliary light source, and a control cabinet; the control cabinet includes Embedded system, system power supply, communication interface, light source excitation controller, line array image acquisition interface. Wherein, the industrial CCD line array camera is connected to the line array image acquisition interface; the auxiliary light source is connected to the light source excitation controller; the system power supply, the communication interface, the light source excitation controller, and the digital line array image acquisition interface are respectively connected to the embedded system.
优选地，所述工业CCD线阵摄像机包括线阵型电荷耦合器件CCD传感器、镜头和数据传输接口。优选地，所述控制机箱安装于列车底盘的下方。Preferably, the industrial CCD line array camera includes a line array charge-coupled device CCD sensor, a lens and a data transmission interface. Preferably, the control cabinet is installed under the train chassis.
优选地，所述嵌入式系统包含嵌入式微处理器、构成微处理器系统的外围接口电路和辅助提高数字图像处理速度的现场可编程逻辑阵列(FPGA)芯片。Preferably, the embedded system includes an embedded microprocessor, a peripheral interface circuit constituting the microprocessor system and a Field Programmable Logic Array (FPGA) chip that assists in improving the speed of digital image processing.
根据本发明的另外一个方面，提供了一种在线钢轨探伤的图像处理辅助检测方法，所述在线钢轨探伤的图像处理辅助检测方法实现步骤包括：第一步、在列车在线运行过程中，嵌入式控制系统控制光源激励控制器使两个辅助光源照射钢轨表面，工业CCD线阵摄像机通过数字线阵图像采集接口的控制采集钢轨表面的图像信息。第二步、数字线阵图像采集接口将图像信息传输到嵌入式系统。第三步、嵌入式系统使用图像处理技术对采集到的钢轨图像进行处理；从处理后的钢轨图像中统计钢轨表面的损伤信息及固有结构信息。第四步、嵌入式系统进一步将所获得的钢轨表面损伤信息进行分类和损伤程度计算。第五步、嵌入式系统将损伤分类信息与损伤程度信息通过通信接口传输到在线钢轨损伤探测装置。第六步、嵌入式系统所传输的信息与在线钢轨损伤探测装置采集信号进行信息融合，从而减少在线钢轨损伤探测装置检测钢轨内部损伤时受表面损伤的干扰程度。According to another aspect of the present invention, an image processing-assisted detection method for on-line rail flaw detection is provided. The implementation steps of the image-processing-assisted detection method for online rail flaw detection include: the first step, during the online operation of the train, the embedded The control system controls the light source excitation controller to make two auxiliary light sources irradiate the rail surface, and the industrial CCD line array camera collects the image information of the rail surface through the control of the digital line array image acquisition interface. In the second step, the digital line array image acquisition interface transmits the image information to the embedded system. In the third step, the embedded system uses image processing technology to process the collected rail images; and calculates the damage information and inherent structure information of the rail surface from the processed rail images. In the fourth step, the embedded system further classifies the obtained rail surface damage information and calculates the damage degree. In the fifth step, the embedded system transmits the damage classification information and damage degree information to the online rail damage detection device through the communication interface. In the sixth step, the information transmitted by the embedded system is fused with the signal collected by the online rail damage detection device, so as to reduce the degree of interference by the surface damage when the online rail damage detection device detects the internal damage of the rail.
优选地，所述第三步的图像处理技术包括钢轨轨缝识别技术和钢轨表面缺陷识别技术，所述第六步的信息融合处理包括加权融合法和系统函数融合法。Preferably, the image processing technology in the third step includes rail gap recognition technology and rail surface defect recognition technology, and the information fusion processing in the sixth step includes weighted fusion method and system function fusion method.
本发明用视频识别的方法来获得钢轨表面的信息，然后用这些信息与重伤探测传感器的信息融合，从而减少表面缺陷的信息干扰。The invention uses the method of video recognition to obtain the information of the rail surface, and then uses the information to fuse with the information of the severe damage detection sensor, thereby reducing the information interference of surface defects.
本发明的有益效果在于：应用了线阵CCD图像采集技术、数字图像处理技术和信息融合方法，解决了在线钢轨探伤装置探测钢轨内部缺陷时受到轨道固有几何结构及表明缺陷的干扰的问题，使在线钢轨探伤装置可以直接输出对轨道交通具有较大潜在危害的钢轨重伤情况。高速钢轨探伤技术的优点是可以在线使用，可及时发现重伤钢轨，所以有重要的应用价值，但由于高速钢轨探伤装置需要装备在高速运行的列车上，所以其传感方式必须采用非接触的方式，这种方式感应钢轨内部重伤，非常容易受到钢轨固有几何结构及表面损伤的影响，在线钢轨探伤的图像处理辅助方法有效解决了这一难题。The beneficial effects of the present invention are: the application of linear array CCD image acquisition technology, digital image processing technology and information fusion method solves the problem that the on-line rail flaw detection device is disturbed by the inherent geometric structure of the rail and the indication defect when detecting the internal defects of the rail. The on-line rail flaw detection device can directly output serious rail injuries that have great potential hazards to rail transit. The advantage of high-speed rail flaw detection technology is that it can be used online and can detect serious rail damage in time, so it has important application value. However, since the high-speed rail flaw detection device needs to be equipped on high-speed trains, its sensing method must be non-contact. , this way of sensing serious damage inside the rail is very susceptible to the influence of the inherent geometric structure and surface damage of the rail. The image processing auxiliary method of online rail flaw detection effectively solves this problem.
附图说明Description of drawings
图1是本发明的在线钢轨探伤的图像处理辅助检测装置的总体结构图。Fig. 1 is an overall structural diagram of the image processing auxiliary detection device for online rail flaw detection according to the present invention.
图2是在线钢轨探伤的图像处理辅助检测装置的安装位置及连接示意图。Figure 2 is a schematic diagram of the installation position and connection of the image processing auxiliary detection device for on-line rail flaw detection.
具体实施方式Detailed ways
下面结合附图对本发明的实施例详述如下：Embodiments of the present invention are described in detail below in conjunction with the accompanying drawings:
如图1所示，一种在线钢轨探伤的图像处理辅助检测装置包括工业CCD线阵摄像机206、辅助光源207、208、控制机箱107；控制机箱中包含嵌入式系统204、系统电源202、通信接口201、光源激励控制器203、数字线阵图像采集接口205。其中，所述工业CCD线阵摄像机206与线阵图像采集接口205连接；辅助光源207、208与光源激励控制器203连接；系统电源202、通信接口201、光源激励控制器203、数字线阵图像采集接口205分别与嵌入式系统204连接。其中：As shown in Figure 1, an image processing auxiliary detection device for online rail flaw detection includes an industrial CCD
工业CCD线阵摄像机206：用于采集钢轨固有几何结构信息和表面缺陷信息，使用线阵采集方式，采集所获得的数字图像信息由通信电缆307传输到数字线阵图像采集接口205。Industrial CCD line array camera 206: used to collect the inherent geometric structure information and surface defect information of the rail, using the line array acquisition method, and the acquired digital image information is transmitted to the digital line array
辅助光源207、208：用于为工业CCD线阵摄像机206提供采集图像时的辅助补光，使用高亮LED阵列作为光源。与光源激励控制器203由导线306和307连接。
控制机箱107：与列车底盘连接，内部包含嵌入式系统204、系统电源202、通信接口201、光源激励控制器203、数字线阵图像采集接口205。Control box 107: connected to the train chassis, which contains an embedded
嵌入式系统204：用于控制图像采集、处理及信息输出，内部包含嵌入式微处理器、图像处理用现场可编程门阵列FPGA芯片。与通信接口201由导线302连接；与光源激励控制器203由导线303连接；与数字线阵图像采集接口205由导线304连接。Embedded system 204: used to control image acquisition, processing and information output, which includes an embedded microprocessor and a field programmable gate array FPGA chip for image processing. It is connected with the
系统电源202：为控制机箱中的各电子设备、辅助光源207和208以及工业CCD线阵摄像机206提供电源。System power supply 202: provide power supply for each electronic equipment in the control cabinet,
通信接口201：用于图像处理后获得的融合信息的输出。Communication interface 201: used for outputting fusion information obtained after image processing.
光源激励控制器203：在嵌入式系统204的控制下，输出控制信号，调节辅助光源207、208的光强。Light source excitation controller 203 : under the control of the embedded
数字线阵图像采集接口205：用于传输工业CCD线阵摄像机206的输出数字图像信息。Digital line array image acquisition interface 205: used to transmit the output digital image information of the industrial CCD
图2是本发明所述装置在列车底部的安装位置及连接示意图，在图2中，控制机箱107、在线钢轨损伤探测装置104和图像辅助处理装置105与列车车厢101连接；图像辅助处理装置105安装于列车车轮102及转向架103的附近，靠近车厢中心的一侧。其中：Fig. 2 is the installation position and the connection schematic diagram of the device of the present invention at the bottom of the train. In Fig. 2, the
列车车厢101：是所述一种在线钢轨探伤的图像处理辅助检测装置的安装载体，可以是客车车厢或货运车厢。Train carriage 101: it is the installation carrier of the image processing auxiliary detection device for on-line rail flaw detection, which may be a passenger carriage or a freight carriage.
车轮102：为列车车轮，此处图示是为了示意所述一种在线钢轨探伤的图像处理辅助检测装置的安装位置。Wheel 102: it is a train wheel, and the illustration here is to illustrate the installation position of the image processing auxiliary detection device for on-line rail flaw detection.
转向架103：是车轮与车厢的连接部件。Bogie 103: It is the connection part between the wheel and the carriage.
在线钢轨损伤探测装置104：是用于列车在线运行时完成钢轨损伤探测的装置，是所述一种在线钢轨探伤的图像处理辅助检测装置输出钢轨固有几何结构及表面损伤信息的接收装置。On-line rail damage detection device 104: it is a device used to complete rail damage detection when the train is running online, and it is a receiving device for the image processing auxiliary detection device of the online rail flaw detection to output the inherent geometric structure and surface damage information of the rail.
图像辅助处理装置105：即所述在线钢轨探伤的图像处理辅助检测装置。Auxiliary image processing device 105: that is, an auxiliary detection device for image processing of the online rail flaw detection.
钢轨106：轨道交通所使用的钢制轨道，一般有锰钢制成。Rail 106: Steel rails used in rail transit, generally made of manganese steel.
在图3中，一种在线钢轨探伤的图像处理辅助检测方法，其实施流程说明如下：In Figure 3, an image processing-assisted detection method for online rail flaw detection, its implementation process is described as follows:
1)系统初始化，设置嵌入式系统204工作参数，通过光源激励控制器203激励辅助光源207和208。1) System initialization, setting the working parameters of the embedded
2)由嵌入式系统204通过数字线阵图像采集接口205设置工业CCD线阵摄像机206的采集速率和线分辨率，工业CCD线阵摄像机206开始采集钢轨表面的图像信息。2) The acquisition rate and line resolution of the industrial CCD
3)工业CCD线阵摄像机206连续输出线阵像素数据，嵌入式系统204将线阵像素序列合成为连续图像，并分组为面阵灰度图像。3) The industrial CCD
4)将分组后的面阵图像并行进行两种运算：识别轨缝和轨面缺陷分析。其中轨缝识别方法包括：分组后的面阵图像边缘检测，图像列向灰度均值向量计算，图像列向灰度均值向量一阶导数和二阶导数计算，轨缝信息提取；轨面缺陷分析包括：分组后的面阵图像增强处理，图像平滑，图像阈值化处理，图像边界抑制，计算钢轨表面损伤程度信息。4) Perform two operations in parallel on the grouped area array images: identification of rail joints and analysis of rail surface defects. Among them, the rail seam recognition method includes: edge detection of grouped area array images, image column-wise gray-scale mean vector calculation, image column-wise gray-scale mean vector first-order derivative and second-order derivative calculation, track seam information extraction; rail surface defect analysis Including: grouped area array image enhancement processing, image smoothing, image thresholding processing, image boundary suppression, and calculation of rail surface damage degree information.
5)钢轨表面图像信息分类及损伤程度计算。5) Classification of rail surface image information and calculation of damage degree.
6)通过通信接口201输出给在线钢轨损伤探测装置，然后返回第步骤3循环处理。6) Output to the online rail damage detection device through the
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AT509607B1 (en) * | 2010-12-13 | 2011-10-15 | Ait Austrian Inst Technology | METHOD AND DEVICE FOR DETECTING SURFACE FAILURES |
| CN102438356A (en) * | 2011-09-20 | 2012-05-02 | 株洲南车时代电气股份有限公司 | Light source adjusting device and method |
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| CN106290379A (en) * | 2016-08-30 | 2017-01-04 | 哈尔滨工业大学（威海） | Rail surface defects based on Surface scan camera detection device and method |
| CN107727660A (en) * | 2017-10-13 | 2018-02-23 | 浙江树人学院 | Rail surface defects detecting system and method based on machine vision and impulse eddy current |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AT509607B1 (en) * | 2010-12-13 | 2011-10-15 | Ait Austrian Inst Technology | METHOD AND DEVICE FOR DETECTING SURFACE FAILURES |
| AT509607A4 (en) * | 2010-12-13 | 2011-10-15 | Ait Austrian Inst Technology | METHOD AND DEVICE FOR DETECTING SURFACE FAILURES |
| CN102438356A (en) * | 2011-09-20 | 2012-05-02 | 株洲南车时代电气股份有限公司 | Light source adjusting device and method |
| CN102507587A (en) * | 2011-09-20 | 2012-06-20 | 株洲南车时代电气股份有限公司 | Perambulated inspection system and method |
| CN102438356B (en) * | 2011-09-20 | 2013-11-27 | 株洲时代电子技术有限公司 | Light source regulating device and method |
| CN102507587B (en) * | 2011-09-20 | 2013-11-27 | 株洲时代电子技术有限公司 | Perambulated inspection system and method |
| CN102565068A (en) * | 2011-12-12 | 2012-07-11 | 关持循 | Steel rail damage automatic detection device |
| CN103884719A (en) * | 2012-12-21 | 2014-06-25 | 鞍钢股份有限公司 | Image acquisition and preprocessing method based on embedded steel plate surface quality detection system |
| CN104237381A (en) * | 2014-10-15 | 2014-12-24 | 北京新联铁科技股份有限公司 | Steel rail flaw-detection method based on laser ultrasonic and high-speed photography image fusion |
| CN104792789A (en) * | 2015-04-08 | 2015-07-22 | 上海常良智能科技有限公司 | Chemical fiber paper tube appearance detection device and method |
| CN106290379A (en) * | 2016-08-30 | 2017-01-04 | 哈尔滨工业大学（威海） | Rail surface defects based on Surface scan camera detection device and method |
| CN107727660A (en) * | 2017-10-13 | 2018-02-23 | 浙江树人学院 | Rail surface defects detecting system and method based on machine vision and impulse eddy current |
| CN108416766A (en) * | 2018-01-31 | 2018-08-17 | 浙江理工大学 | Visual inspection method for defects of double-side incident light guide plate |
| CN108416766B (en) * | 2018-01-31 | 2021-10-22 | 杭州衡眺科技有限公司 | Double-side light-entering type light guide plate defect visual detection method |
| CN109490416A (en) * | 2018-12-10 | 2019-03-19 | 上海市东方海事工程技术有限公司 | A kind of weld joint recognition method applied to double rail type rail examination |
| CN109677444A (en) * | 2019-01-10 | 2019-04-26 | 北京交通大学 | A kind of acquisition device of the comprehensive perception of track apparent condition |
| CN109975315A (en) * | 2019-04-29 | 2019-07-05 | 南昌工程学院 | A visual inspection vehicle for surface defects of track components |
| CN110987942A (en) * | 2019-12-04 | 2020-04-10 | 联想(北京)有限公司 | Electronic device and machine vision system |
| CN112785891A (en) * | 2021-01-13 | 2021-05-11 | 中国铁路昆明局集团有限公司 | Steel rail flaw detection simulation learning practical training instrument, training system platform and practical training method |
| CN112785891B (en) * | 2021-01-13 | 2022-11-11 | 中国铁路昆明局集团有限公司 | Steel rail flaw detection simulation learning training system platform and training method |
| CN113111875A (en) * | 2021-04-02 | 2021-07-13 | 广州地铁集团有限公司 | Seamless steel rail weld defect identification device and method based on deep learning |
| CN113111875B (en) * | 2021-04-02 | 2024-08-13 | 广州地铁集团有限公司 | Seamless steel rail weld defect recognition device and method based on deep learning |
| CN113484328A (en) * | 2021-08-04 | 2021-10-08 | 湖南铁路科技职业技术学院 | Novel steel rail flaw detector combining machine vision with ultrasonic technology |
| CN113484328B (en) * | 2021-08-04 | 2022-08-26 | 湖南铁路科技职业技术学院 | Novel steel rail flaw detector combining machine vision with ultrasonic technology |
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