https://nottingham-repository.worktribe.com/preview/745918/Manuscript_KochEtAl_2015_accepted.pdf

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Cracks on the concrete surface are one of the earliest indications of degradation of the structure which is critical for the maintenance as well the continuous exposure will lead to the severe damage to the environment. Manual inspection is the acclaimed method for the crack inspection. In the manual inspection, the sketch of the crack is prepared manually, and the conditions of the irregularities are noted. Since the manual approach completely depends on the specialist’s knowledge and experience, it lacks objectivity in the quantitative analysis. So, automatic image-based crack detection is proposed as a replacement. Literature presents different techniques to automatically identify the crack and its depth using image processing techniques. In this research, a detailed survey is conducted to identify the research challenges and the achievements till in this field. Accordingly, 50 research papers are taken related to crack detection, and those research papers are reviewed. Based on the review, analysis is provided based on the image processing techniques, objectives, accuracy level, error level, and the image data sets. Finally, we present the various research issues which can be useful for the researchers to accomplish further research on the crack detection.

Crack detection using image processing: A critical review and analysis

The detection of cracks on concrete surfaces is the most important step during the inspection of concrete structures. Conventional crack detection methods are performed by experienced human inspectors who sketch crack patterns manually; however, such detection methods are expensive and subjective. Therefore, automated crack detection techniques that utilize image processing have been proposed. Although most the image-based approaches focus on the accuracy of crack detection, the computation time is also important for practical applications because the size of digital images has increased up to 10 megapixels. We introduce an efficient and high-speed crack detection method that employs percolation-based image processing. We propose termination- and skip-added procedures to reduce the computation time. The percolation process is terminated by calculating the circularity during the processing. Moreover, percolation processing can be skipped in subsequent pixels according to the circularity of neighboring pixels. The experimental result shows that the proposed approach efficiently reduces the computation cost.

Fast crack detection method for large-size concrete surface images using percolation-based image processing

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

https://www.mdpi.com/1424-8220/11/10/9628/pdf

Adaptive Road Crack Detection System by Pavement Classification

Image-based retrieval of concrete crack properties for bridge inspection

A robust approach for automatic detection and segmentation of cracks in underground pipeline images

: Assessing the condition of underground pipelines such as water lines, sewer pipes, and telecommunication conduits in an automated and reliable manner is vital to the safety and maintenance of buried public infrastructure. To fully automate condition assessment, it is necessary to develop robust data analysis and interpretation systems for defects in buried pipes. This article presents the development of an automated data analysis system for detecting defects in sanitary sewer pipelines. We propose a three-step method to identify and extract cracks from contrast enhanced pipe images. This method is based on mathematical morphology and curvature evaluation that detects crack-like patterns in a noisy pipe camera scanned image. As cracks are the most common defects in pipes and are indicative of the residual structural strength of the pipe, they are the focus of this study. This article discusses its implementation on 225 pipe images taken from different cities in North America and shows that the system performs very well under a variety of pipe conditions.

Segmentation of Pipe Images for Crack Detection in Buried Sewers

Ultrasonic signal processing methods for detection of defects in concrete pipes

Evaluation of ultrasonic inspection and imaging systems for concrete pipes

: Corrosion of the nation's transportation infrastructure is a widespread and costly problem. Efficiency, durability, safety, and environmental concerns have made the inspection and structural assessment of these structures a vital issue. The current state of the art in concrete bridge condition evaluation relies on visual inspection. However, deterioration in pre-stressing/post-tensioned strand or tendon condition is not always reflected by distress visible on the concrete surface. Further, the effect of deterioration of pre-stressing/post-tensioned steel is more disruptive than that of mild reinforcement. Strand, due to its high mechanical strength and metallurgical characteristics, is smaller in cross section than conventional reinforcing steel and is proportionally more impaired by loss of section. Methodology for pre-stressed concrete bridge condition evaluation, therefore, could be revolutionized through the development of accurate, quantitative nondestructive test methods for strand in pre-tensioned and post-tensioned structures. This article presents a new sensing method using ultrasound C-scan imaging for structural health monitoring of post-tensioned bridges. Preliminary results from tests are presented that show promising potential for the detection of corrosion and voids in concrete post-tensioned bridges.

Shivprakash Iyer

Papers

A robust approach for automatic detection and segmentation of cracks in underground pipeline images

Segmentation of Pipe Images for Crack Detection in Buried Sewers

Ultrasonic signal processing methods for detection of defects in concrete pipes

Evaluation of ultrasonic inspection and imaging systems for concrete pipes

Ultrasonic C-scan imaging of post-tensioned concrete bridge structures for detection of corrosion and voids