수도 레이블을 활용한 준지도 학습 기반의 도로노면 파손 탐지
Road Surface Damage Detection Based on Semi-supervised Learning Using Pseudo Labels
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참고문헌
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초록
의미론적 분할 형태로 합성곱 신경망을 구성하여 도로노면의 파손을 탐지하는 연구가 진행되고 있다. 이러한 합성곱 신경망 형태의 모델을 생성하기 위해서는 입력 이미지와 이에 상응한 레이블된 이미지 데이터셋으로 수집해야 하고, 이러한 과정에서는 굉장히 많은 시간과 비용이 발생하게 된다. 본 논문에서는 이러한 작업을 완화하기 위하여 수도 레이블링을 활용한준지도 학습 기반의 도로노면 파손 탐지 기술을 제안하고자 한다. 레이블된 데이터셋과 레이블되지 않은 데이터셋을 적절하게 혼합하여 도로노면 파손을 탐지하는 모델을 업데이트하고, 이를 레이블된 데이터셋만을 활용한 기존 모델과 성능을 비교한다. 주관적인 성능결과, 민감도부분에서는 조금 저하된 성능을 보였지만, 정밀도 부분에서는 대폭 성능 향상이 있었으며, 최종적으로 F1-score 또한 높은 수치로 평가되었다.
Abstract
By using convolutional neural networks (CNNs) based on semantic segmentation, road surface damage detection has being studied. In order to generate the CNN model, it is essential to collect the input and the corresponding labeled images. Unfortunately, such collecting pairs of the dataset requires a great deal of time and costs. In this paper, we proposed a road surface damage detection technique based on semi-supervised learning using pseudo labels to mitigate such problem. The model is updated by properly mixing labeled and unlabeled datasets, and compares the performance against existing model using only labeled dataset. As a subjective result, it was confirmed that the recall was slightly degraded, but the precision was considerably improved. In addition, the F1-score was also evaluated as a high value.
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