Development of Automatic Crack Detection System for Concrete Structure Using Image Processing Method

Lee, Ho Beom; Kim, Jong Woo; Jang, Il‐Young

doi:10.11112/jksmi.2012.16.1.064

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most early studies, crack features were extracted and detected using morphological methods, which are image processing techniques utilizing the images' morphological computation [9,10,[16][17][18]. In other studies, researchers have applied a fuzzy technique to the RGB (Red, Green, Blue) channel values in images by utilizing the contrast in the intensity of the cracks [12,19], improving the contrast characteristics of images by applying a histogram stretching method [20], and using image processing methods such as edge detection and noise removal [21][22][23][24].…”

Section: Previous Research On Crack Diagnosismentioning

confidence: 99%

Ontological Approach for Automatic Inference of Concrete Crack Cause

Jung

Lee

2020

Applied Sciences

View full text Add to dashboard Cite

The cause of cracks in concrete is traditionally estimated by analyzing information such as patterns and locations of the cracks and whether other defects are present, followed by aggregating the findings to estimate the cause. This method is highly dependent on the expert’s knowledge and experience in the process of identifying the cause of the cracks by compiling information related to the occurrence of the cracks, and it is likely that each expert will make a different diagnosis or an expert with insufficient knowledge and experience will make an inaccurate diagnosis. Therefore, we propose automated technology using the ontology to improve the consistency and accuracy of crack diagnosis results in this research. The proposed approach uses information on the crack patterns, locations, and penetration status, as well as the occurrence of other defects, to automatically infer the causes of cracks. We developed ontology that can infer the cause of cracks using the information on their appearance and applied actual cases of cracks to verify the ontological operation. In addition, the consistency and accuracy of the ontology were validated using eight actual cases of crack. The approach of this study can support expert decision-making in the crack diagnosis process, thereby reducing the possibility of various errors caused by the intervention of inaccurate judgments in the crack diagnosis process and improving the efficiency of the crack diagnosis tasks.

show abstract

Section: Previous Research On Crack Diagnosismentioning

confidence: 99%

Ontological Approach for Automatic Inference of Concrete Crack Cause

Jung

Lee

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The enhancement of the resolution of imaging equipment and the development of optical equipment has enabled the detection of cracks from long distances, which has led to studies on crack measurement. In 2012, Lee detected cracks in structures and measured the widths, lengths, and directions of the cracks using an 18-megapixel camera and a 600 mm lens, and concluded that 0.2 mm cracks can be detected at a working distance of 40 m [19]. In 2014, Li secured a working distance of 60 m in a study that applied a focal length of 1000 mm using a 20-megapixel video camera, a 500 mm lens, and a 2X converter, and showed an error rate of 92.6% [20].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera

Jeong

Han

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Visual inspections are performed to investigate cracks in concrete infrastructure. These activities require manpower or equipment such as articulated ladders. Additionally, there are health and safety issues because some structures have low accessibility. To deal with these problems, crack measurement with digital images and digital image processing (DIP) techniques have been adopted in various studies. The objective of this experimental study is to evaluate the optical limit of digital camera lenses as working distance increases. Three different lenses and two digital cameras were used to capture images of lines ranging from 0.1 to 0.5 mm in thickness. As a result of the experiments, it was found that many elements affect width measurement. However, crack width measurement is dependent on the measured pixel values. To accurately measure width, the measured pixel values must be in decimal units, but that is theoretically impossible. According to the results, in the case of 0.3 mm wide or wider cracks, a working distance of 1 m was secured when the focal length was 50 mm, and working distances of 3 m and 4 m were secured when the focal length was 100 mm and 135 mm, respectively. However, for cracks not wider than 0.1 mm, focal lengths of 100 mm and 135 mm showed measurability within 1 m, but a focal length of 50 mm was judged to hardly enable measurement except for certain working positions. Field measurement tests were conducted to verify measurement parameters identified by the results of the indoor experiment. The widths of actual cracks were measured through visual inspection and used for the analysis. From the evaluation, it was confirmed that the number of pixels corresponding to the working distance had a great influence on crack width measurement accuracy when using image processing. Therefore, the optimal distance and measurement guidelines required for the measurement of the size of certain objects was presented for the imaging equipment and optical equipment applied in this study.

show abstract