모션블러 이미지에 대한 CNN 모델의 균열 검출 성능
Crack detection performance of CNN models on motion blur images
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초록
본 연구에서는 터널 콘크리트 라이닝에 발생하는 균열을 검출하는데 활용되는 이미지에 발생하는 모션블러가 CNN 모델들의 성능에 미치는 영향을 분석하였다. Kaggle과KICT 데이터셋에 10~50 범위의 강도를 갖는 모션블러 이미지를 생성하고, U-Net 아키텍처에 사전 학습된 ResNet 18, ResNet 34, VGG 11 및 AlexNet를 특징추출을 위한 백본으로 사용한 의미론적 분할 모델을 활용하여 균열 검출 성능을 분석하였다. 각각의 데이터셋에 모션블러가 증가함에 따라 모든 모델에서 검출 정확도가 감소하는 것으로 나타났다. 동일한 학습 모델에서 KICT 데이터셋의 F1-score가Kaggle 데이터셋보다 20% 이상 높은 성능을 갖는 것으로 평가되었다. 본 연구의 결과로, CNN기반 균열 검출 성능은 이미지 데이터의 품질에 따라 차이가 발생하며, 기 구축된 학습 데이터셋의 품질에 따라서도 CNN 모델의 균열 검출 성능이 상이할 수 있는 것으로 판단된다.
Abstract
In this study, we analyzed the effect of motion blur on images used for detecting cracksin concrete tunnel linings on the performance of CNN models. Motion-blurred imageswith intensities ranging from 10 to 50 were generated on the Kaggle and KICT datasets. A semantic segmentation model with ResNet 18, ResNet 34, VGG 11, and Alex-Net as backbones for feature extraction was employed, all pre-trained on the U-Netarchitecture. The performance of these models in crack detection was then assessed. It was observed that detection accuracy decreased across all models as the intensity ofmotion blur increased for each dataset. Within the same model, the F1-score on theKICT dataset showed over 20% higher performance than on the Kaggle dataset. Thisstudy demonstrates that CNN-based crack detection performance is affected by thequality of the image data and that the crack detection accuracy of CNN models canvary depending on the quality of the dataset used in training.
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