Pavement Crack Recognition Based on Aerial Image
航拍图像的路面裂缝识别
作者: Wang BoWang XiaChen FeiHe YuntaoLi WenguangLiu LiKey Laboratory of Optoelectronic Imaging Technology and System,Ministry of Education,School of Optoelectronics,Beijing Institute of TechnologySchool of Aerospace Engineering,Beijing Institute of Technology
刊名: Acta Optica Sinica, 2017(08), pp.126-132
中文刊名: 光学学报, 2017(08), pp.126-132
来源数据库: CJFDTEMP_U
关键词: image processingaerial object detectionpavement crackregional growth based on multi-directional fittingmorphological filteringsaliency analysis
中文关键词: 图像处理航拍目标检测路面裂缝多方向拟合区域生长形态学滤波显著性分析
英文摘要: Aiming at the problems of interference and noise in image recognition of aerial asphalt pavement,apavement crack recognition algorithm applied to aerial image is put forward.According to the difference of gray level distribution of the surface area and the roadside landscape area,a method of regional growth based on multi-directional fitting and threshold segmentation in HSV color space for road region segmentation is proposed.The single channel pavement which contains integral crack information is extracted,the large area of interference is eliminated by the improved morphological filtering,and an edge detection algorithm based on saliency analysis to recognise the crack fragment of pavement is proposed,realizing the distinction between complex cracks and pavement texture noise.The...
中文摘要: 针对航拍沥青路面图像识别的噪声和干扰问题,提出一种应用于航拍图像的路面裂缝识别算法。根据路面区域与路旁景观区域灰度级数分布不同,采用多方向拟合的区域生长方法联合HSV颜色空间阈值进行路面区域分割,提取包含完整裂缝信息的单通道路面;再通过改进的形态学滤波剔除面积较大的干扰区域,利用结合显著性分析的边缘检测算法识别路面的裂缝片段,实现复杂裂缝与路面纹理噪声的区分;自动筛选存在裂缝的图像,针对裂缝可疑区域,结合人眼辅助观察标记并计算其长度。结果表明,该算法可有效剔除图像中的噪声和干扰,较好地识别沥青路面的裂缝,裂缝宽度的识别精度能达到9.7mm,分类识别准确率大于80.0%,长度测量准确率大于75.0%。
全文获取路径:
分享到:

×
关键词翻译
关键词翻译