Efficient pavement crack detection and classification (original) (raw)

IJERT-Hough transforms to detect and classify road cracks IJERTV3IS

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/hough-transforms-to-detect-and-classify-road-cracks https://www.ijert.org/research/hough-transforms-to-detect-and-classify-road-cracks-IJERTV3IS061357.pdf Abstrac-A system for road crack detection and characterization is proposed here to minimize the human subjectivity as in the customary overviews routines. There are three tasks addressed here .The first task is crack detection for which Hough transformation used. This based on learning from samples images, where a subset of the available image database is selected and used for supervised training of the system. The second task deals with crack type characterization, for which SVM classifier is us e d, to characterize the detected cracks as to which type of crack it is. A s t h e t h i r d t a s k a methodology for the assignment of crack severity levels is introduced, computing an estimate for the average width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Indian highways.

A neural network-based methodology for pavement crack detection and classification

Transportation Research Part C-emerging Technologies, 1993

This paper presents a methodology for automating the processingof highway pavement video images using an integration of artificial neural network models with conventional image-processing techniques. The methodology developed is able to classify pavement surface cracking by the type, severity, and extent of cracks detected in video images. The approach is divided into five major steps: (1) image segmentation, which involves reduction of a raw gray-scale pavement image into a binary image, (2) feature extraction, (3) decomposition of the image into tiles and identification of tiles with cracking, (4) integration of the results from step (3) and classification of the type of cracking in each image, and (5) computation of the severities and extents of cracking detected in each image. In this methodology, artificial neural network models are used in automatic thresholding of the images in stage (1) and in the classification stages (3) and (4). The results obtained in each stage of the process are presented and discussed in this paper. The research results demonstrate the feasibility of this new approach for the detection, classification, and quantification of highway pavement surface cracking.