【作者】 王成；

【导师】 聂明新；

【作者基本信息】 武汉理工大学 ， 信息与通信工程， 2019， 硕士

【摘要】 近年来,随着经济的飞速发展和科学技术水平的进步,一方面,人们出行更加便利,另一方面,公路的健康也因车载的加重、恶劣的天气、自然老化等因素的影响而越来越差。裂缝是影响路面性能的常见路面病害,传统的人工检测方法不仅耗时、费力、准确率低而且安全性低。因此,自动裂缝检测识别系统的研究对于确保交通的安全具有重要意义。目前已经有许多学者在自动裂缝检测识别方面作了研究,但由于路面裂缝图像的光强不均匀性、裂缝的拓扑复杂性、纹理背景嘈杂等缺点,使得目前路面裂缝识别方法的准确率普遍偏低,而且由于计算时间复杂度大多较高,导致无法及时的检测出裂缝,难以满足实际要求。针对人工现场检测方法的不安全性以及传统图像处理裂缝识别中低精度等问题,本文以提高裂缝识别率以及实时检测裂缝为目的,开展了如下研究工作:(1)传统的裂缝检测通常采用现场拍照,线下识别的方法对裂缝进行检测,为了实现对路面裂缝的实时在线检测,本文对基于深度学习的路面裂缝检测进行了研究与实现。(2)为了实现路面裂缝图像的快速检测识别,采用一种基于YOLO v3的深度学习网络对路面裂缝进行检测。在此基础上,为了实现对裂缝视频的检测,运用OpenCV对视频进行帧提取,在模型检测后,将视频帧还原成视频,从而完成裂缝的实时检测。(3)由于采集的裂缝图像偶尔会有背景纹理等噪声而导致模型难以检测出裂缝,并且这类裂缝图像的数据集很少,无法用模型训练来提高这类裂缝的检测准确率。针对这种情形,运用多尺度方法处理图像噪声。首先通过非下采样Contourlet变换对图像进行处理,根据裂缝与背景纹理的不同频域特征,将噪声信号去除掉,接着通过反非下采样Contourlet变换进行图像的重构,得到去噪后的图像,最后对去噪后的裂缝图像进行检测识别。(4)对路面裂缝检测系统的软件部分进行了研究与设计,主要包括用户界面模块、视频分割模块、裂缝检测模块、数据库模块等。并且对系统的各个模块进行测试。测试结果表明,系统能够完成路面裂缝的检测,符合预期。更多还原

【Abstract】 In recent years,with the rapid development of the economy and the advancement of science and technology,On the one hand,people travel more conveniently.On the other hand,the health of roads is getting worse due to the aggravation of the car,bad weather,natural aging and other factors.Cracks are common road diseases that affect road performance.Traditional manual detection methods are not only time-consuming,labor-intensive,low-accuracy,and low in safety.Therefore,the research of automatic crack detection and identification system is of great significance to ensure the safety of traffic.At present,many scholars have done research on automatic crack detection and identification.However,the accuracy of the current pavement crack identification is generally low due to the unevenness of the light intensity of the road surface crack,the topological complexity of the crack,and the noisy texture background,and because the calculation time complexity is mostly high,it is impossible to detect the crack in time,and it is difficult to meet the actual requirements.In view of the shortcomings of traditional manual detection methods and the problems in traditional image processing crack identification,this paper carried out the following research work to improve the crack recognition rate and detect cracks in real time:(1)The traditional crack detection usually uses on-the-spot photographing and offline identification to detect cracks.In order to realize real-time on-line detection of pavement cracks,this paper studies and implements the pavement crack detection based on deep learning.(2)In order to realize the rapid detection and recognition of pavement crack images,a deep learning network based on YOLO v3 is used to detect pavement cracks.On this basis,in order to realize the detection of crack video,OpenCV is used to frame the video and identify it with the model,then the video frame is restored to video,thus the real-time detection of crack is realized.(3)Since the collected crack images occasionally have noise such as background texture,it is difficult for the model to detect cracks,and the data set of such crack images is very small,and model training cannot be used to improve the detection accuracy of such cracks.In response to this situation,multi-scale methods are used to process image noise.Firstly,the image is processed by non-subsampled Contourlet transform.The noise signal is removed according to the different frequency domain features of the crack and the background texture,and then the image is reconstructed by the inverse non-subsampled Contourlet transform to obtain the denoised image.Finally,the crack image after denoising is detected and identified.(4)The software part of the pavement crack detection system is researched and designed,including user interface module,video segmentation module,crack detection module,database module,etc.And each module of the system is tested.The test results show that the system can complete the detection of pavement cracks and achieve the expected results.更多还原