TY - GEN
T1 - Automatic pavement crack detection based on image recognition
AU - Chen, C.
AU - Seo, H. S.
AU - Zhao, Y.
AU - Chen, B.
AU - Kim, J. W.
AU - Choi, Y.
AU - Bang, M.
N1 - Publisher Copyright:
© International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making.
PY - 2019
Y1 - 2019
N2 - The traffic jam influences lots of losses socially and economically. One of main reason of traffic jam is caused by the maintenance of the pavement. It is better to detect and repair cracks on time to avoid big damage to pavement Therefore, lots of researches have been started to identify the crack with several methods. One method which is widely used is damage detection using image processing. Due to various complicated road conditions, such as oil pollution on the surface of the pavement and image noise, the classical algorithm cannot meet the detection requirements, the cracks cannot be extracted well. To solve the problems above, first, this paper introduces the mean filtering method to remove the image noise preliminarily and histogram equalization method to enhance the contrast of pavement image. Then, a pre-treatment before threshold segmentation was proposed to remove shadows and image noise. Then the crack is extracted by Niblack segmentation method and the image noise is filtered by removing the small area of the connected component. Finally, the crack type is classified using SVM (Support Vector Machine) and the location is obtained using the timeline to match GPS information and image.
AB - The traffic jam influences lots of losses socially and economically. One of main reason of traffic jam is caused by the maintenance of the pavement. It is better to detect and repair cracks on time to avoid big damage to pavement Therefore, lots of researches have been started to identify the crack with several methods. One method which is widely used is damage detection using image processing. Due to various complicated road conditions, such as oil pollution on the surface of the pavement and image noise, the classical algorithm cannot meet the detection requirements, the cracks cannot be extracted well. To solve the problems above, first, this paper introduces the mean filtering method to remove the image noise preliminarily and histogram equalization method to enhance the contrast of pavement image. Then, a pre-treatment before threshold segmentation was proposed to remove shadows and image noise. Then the crack is extracted by Niblack segmentation method and the image noise is filtered by removing the small area of the connected component. Finally, the crack type is classified using SVM (Support Vector Machine) and the location is obtained using the timeline to match GPS information and image.
UR - http://www.scopus.com/inward/record.url?scp=85091018733&partnerID=8YFLogxK
U2 - 10.1680/icsic.64669.361
DO - 10.1680/icsic.64669.361
M3 - Conference Proceeding
AN - SCOPUS:85091018733
T3 - International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making
SP - 361
EP - 369
BT - International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019
A2 - DeJong, M.J.
A2 - Schooling, Jennifer M.
A2 - Viggiani, G.M.B.
PB - ICE Publishing
T2 - 2nd International Conference on Smart Infrastructure and Construction: Driving Data-Informed Decision-Making, ICSIC 2019
Y2 - 1 July 2019 through 3 July 2019
ER -