합성곱 신경망을 이용한 아스팔트 콘크리트 도로포장 표면균열 검출
Asphalt Concrete Pavement Surface Crack Detection using Convolutional Neural Network
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초록
본 연구에서는 아스팔트 콘크리트 도로포장의 표면균열 검출을 위해 합성곱 신경망을 이용하였다. 합성곱 신경망의 학습에 사용되는 표면균열 이미지 데이터의 양에 따른 합성곱 신경망의 성능향상 정도를 평가하였다. 사용된 합성곱 신경망의 구조는 5개의 층으로 구성되어있으며, 3x3 크기의 convolution filter와 2x2 크기의 pooling kernel을 사용하였다. 합성곱 신경망의 학습을 위해서 도로노면 조사 장비를 통해 구축된 국내 도로포장 표면균열 이미지를 활용하였다. 표면균열 이미지 데이터를 학습한 합성곱 신경망 모델의 표면균열 검출 정확도, 정밀도, 재현율, 미검출율, 과검출율을 평가하였다. 가장 많은 양의 데이터를 학습한 합성곱 신경망 모델의 표면균열 검출 정확도, 정밀도, 재현율은 96.6% 이상, 미검출율, 과검출율은 3.4% 이하의 성능을 나타내었다.
Abstract
A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3x3 convolution filter and 2x2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.
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