자동화 균열 탐지 시스템을 위한 딥러닝 모델에 관한 연구
Deep Learning Models for Autonomous Crack Detection System
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초록
균열은 건물, 교량, 도로, 수송관 등의 기반시설의 안전성에 영향을 주는 요소이다. 본 연구에서는 검사 비용과 시간을 줄일 수 있는 자동화된균열 탐지 시스템을 다룬다. 환경과 표면에 강건한 시스템을 구성하기 위해서, 본 연구에서는 여러 사전 연구에서 사용된 다양한 표면의 균열데이터 셋을 수집하여 통합 데이터 셋을 구축하였다. 이후, 컴퓨터 비전 분야에 높은 성능을 발휘하는 VGG, ResNet, WideResNet, ResNeXt,DenseNet, EfficientNet 딥러닝 모델을 적용하였다. 통합 데이터 셋은 훈련 집합(80%)과 테스트 집합(20%)으로 나누어 모델 성능을 검증하기 위해서사용했다. 실험 결과, DenseNet121 모델이 높은 마라미터 효율성을 가지면서도 테스트 집합에 대해 96.20%의 정확도를 달성하여 가장 높은 성능을보여주었다. 딥러닝 모델의 균열 검출 성능 검증을 통해, DenseNet121를 활용하여 컴퓨팅 자원이 적은 소형 디바이스에서도 높은 균열 검출 성능을보이는 탐지 시스템을 구축이 가능함을 확인했다.
Abstract
Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automatedcrack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiplecrack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks includingVGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We dividedthe combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracyat 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deeplearning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be appliedto different surfaces and structures with low computing resources.
참고문헌 (24)
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