列車前方軌道識別算法的設(shè)計(jì)與實(shí)現(xiàn)
發(fā)布時(shí)間：2019-01-09 11:43
【摘要】：近年來,隨著城市軌道交通的快速發(fā)展,地鐵、輕軌等列車的行車安全變得日益重要。障礙物檢測系統(tǒng)通過輔助駕駛員排查列車前方軌道情況,提高了列車的行車安全性。對于障礙物檢測系統(tǒng),高效準(zhǔn)確地識別出列車前方軌道至關(guān)重要。本文基于列車車載前置攝像頭采集到的圖像,完成了對列車前方軌道的識別。識別過程主要分兩步,首先是近距離軌道識別,然后是根據(jù)近距離軌道識別的結(jié)果得到種子點(diǎn),以改進(jìn)的引入方向的種子區(qū)域增長方法完成遠(yuǎn)距離軌道識別。對于近距離里軌道識別,本文采用的是現(xiàn)有的基于曲率映射圖的軌道識別算法,并作了進(jìn)一步的改進(jìn)。其中,曲率映射圖類似于模板匹配中的模板,不過更為細(xì)致。本文的改進(jìn)主要體現(xiàn)在曲率映射圖的創(chuàng)建上,在創(chuàng)建曲率映射圖之前,建立了列車與不同曲率的理想軌道之間的位置關(guān)系。然后根據(jù)相機(jī)的內(nèi)參及其相對列車的位置與姿態(tài)得到圖像上每個(gè)像素點(diǎn)對應(yīng)的理想軌道的曲率,即曲率映射圖。整個(gè)過程不僅計(jì)算方便,而且得到的曲率映射圖精度高。另外,本文在根據(jù)曲率映射圖和輸入圖像的梯度圖選取最匹配的理想曲率時(shí),也作了一定的改進(jìn)。對于遠(yuǎn)距離軌道識別,本文提出了基于局部梯度信息的軌道識別算法,該方法一共分四步。首先根據(jù)圖像梯度,設(shè)計(jì)一個(gè)度量,衡量某一區(qū)域內(nèi)的圖像與實(shí)際軌道圖像的相似度。然后根據(jù)近距離軌道識別的結(jié)果,得到初始的種子點(diǎn)(包括位置和方向)。接著在當(dāng)前初始種子點(diǎn)的鄰域內(nèi)搜索一個(gè)最佳的位置和方向(相似度最高,并滿足一定約束的)作為當(dāng)前最佳種子點(diǎn),并由當(dāng)前最佳種子點(diǎn)延伸到下一個(gè)初始種子點(diǎn)。最后重復(fù)種子延伸過程,并聯(lián)合左右鋼軌一同進(jìn)行,直到找不到滿足一定約束的最佳種子點(diǎn),從而完成遠(yuǎn)距離軌道的識別。此外,本文還提出了基于兩條已知間距的平行線的相機(jī)外參標(biāo)定算法。該方法根據(jù)曲率為0,坡度不變的直線型軌道在圖像上的位置標(biāo)定得到相機(jī)外參,標(biāo)定過程方便快捷。
[Abstract]:In recent years, with the rapid development of urban rail transit, the safety of trains such as subway and light rail has become increasingly important. The obstacle detection system improves the safety of the train by assisting the driver to check the track in front of the train. For the obstacle detection system, it is very important to identify the track in front of the train efficiently and accurately. Based on the image collected by the front camera of the train, the recognition of the track in front of the train is completed. The recognition process is mainly divided into two steps: first, the close orbit recognition, and then the seed points are obtained according to the results of the close orbit recognition, and the improved seed region growth method with introduced direction is used to complete the long distance orbit recognition. For the near distance orbit recognition, this paper uses the existing curvature mapping graph based orbit recognition algorithm, and makes further improvements. Among them, curvature map is similar to template in template matching, but more detailed. The improvement of this paper is mainly reflected in the creation of the curvature mapping graph. Before the curvature mapping graph is created, the position relationship between the train and the ideal track with different curvature is established. Then the curvature of the ideal orbit corresponding to each pixel in the image is obtained according to the camera's internal parameters and the relative position and attitude of the train, that is, the curvature map. The whole process is not only easy to calculate, but also has high precision of curvature map. In addition, this paper also makes some improvements in selecting the most suitable ideal curvature according to the curvature mapping graph and the gradient map of the input image. For long distance orbit recognition, an algorithm based on local gradient information is proposed, which is divided into four steps. Firstly, according to the image gradient, a measure is designed to measure the similarity between the image in a certain region and the actual track image. Then the initial seed points (including position and direction) are obtained according to the results of close orbit recognition. Then we search the neighborhood of the current initial seed point for an optimal position and direction (with the highest similarity and satisfying certain constraints) as the current best seed point and extend from the current best seed point to the next initial seed point. Finally, the process of seed extension is repeated and combined with the left and right rails, until the best seed point can not be found to meet certain constraints, thus the identification of the long distance orbit is completed. In addition, a camera external parameter calibration algorithm based on two known parallel lines is proposed. In this method, the camera parameters can be obtained according to the position of the linear track with a curvature of 0 and a constant slope on the image, and the calibration process is convenient and fast.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U284.48;TP391.41
本文編號：2405576
[Abstract]:In recent years, with the rapid development of urban rail transit, the safety of trains such as subway and light rail has become increasingly important. The obstacle detection system improves the safety of the train by assisting the driver to check the track in front of the train. For the obstacle detection system, it is very important to identify the track in front of the train efficiently and accurately. Based on the image collected by the front camera of the train, the recognition of the track in front of the train is completed. The recognition process is mainly divided into two steps: first, the close orbit recognition, and then the seed points are obtained according to the results of the close orbit recognition, and the improved seed region growth method with introduced direction is used to complete the long distance orbit recognition. For the near distance orbit recognition, this paper uses the existing curvature mapping graph based orbit recognition algorithm, and makes further improvements. Among them, curvature map is similar to template in template matching, but more detailed. The improvement of this paper is mainly reflected in the creation of the curvature mapping graph. Before the curvature mapping graph is created, the position relationship between the train and the ideal track with different curvature is established. Then the curvature of the ideal orbit corresponding to each pixel in the image is obtained according to the camera's internal parameters and the relative position and attitude of the train, that is, the curvature map. The whole process is not only easy to calculate, but also has high precision of curvature map. In addition, this paper also makes some improvements in selecting the most suitable ideal curvature according to the curvature mapping graph and the gradient map of the input image. For long distance orbit recognition, an algorithm based on local gradient information is proposed, which is divided into four steps. Firstly, according to the image gradient, a measure is designed to measure the similarity between the image in a certain region and the actual track image. Then the initial seed points (including position and direction) are obtained according to the results of close orbit recognition. Then we search the neighborhood of the current initial seed point for an optimal position and direction (with the highest similarity and satisfying certain constraints) as the current best seed point and extend from the current best seed point to the next initial seed point. Finally, the process of seed extension is repeated and combined with the left and right rails, until the best seed point can not be found to meet certain constraints, thus the identification of the long distance orbit is completed. In addition, a camera external parameter calibration algorithm based on two known parallel lines is proposed. In this method, the camera parameters can be obtained according to the position of the linear track with a curvature of 0 and a constant slope on the image, and the calibration process is convenient and fast.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U284.48;TP391.41
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