한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제18권3호
  • /
  • Pages.95-105
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  • 2019
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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도로 노면 파손 탐지를 위한 배경 객체 인식 기반의 지도 학습을 활용한 성능 향상 알고리즘

Performance Enhancement Algorithm using Supervised Learning based on Background Object Detection for Road Surface Damage Detection

  • Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology) ;
  • Chun, Chanjun (Korea Institute of Civil Engineering and Building Technology) ;
  • Ryu, Seung-Ki (Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2019.04.08
  • 심사 : 2019.04.29
  • 발행 : 2019.06.30
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초록

최근 들어 도로 노면 파손의 위치 정보를 수집하기 위한 영상 처리 기술에 대한 연구가 활발히 진행되고 있다. 대표적으로 차량에 탑재가 가능한 스마트폰이나 블랙박스를 통해 영상을 얻고 이를 영상처리 알고리즘을 사용하여 인식하는 기술이 주로 사용된다. GPS 모듈과 연계하여 실제 파손 위치를 파악할 때 가장 중요한 기술은 영상 처리 알고리즘인데, 근래에는 대부분 인공지능을 통한 알고리즘이 연구 주제로 주목받고 있다. 이와 같은 맥락에서 본 연구에서도 영역 기반의 합성곱 방식 계열의 객체인식 (Object Detection) 방법을 사용한 인공지능 영상 처리 알고리즘에 대하여 논의하고자 한다. 도로 노면 파손 객체 인식 성능을 향상시키기 위하여 도로 노면 파손 영상 600여 장과 일반적인 도로 주행 영상 1500여 장으로 학습 데이터베이스를 구성하였다. 또한 배경 객체 인식 방법을 적용한 지도 학습을 수행하여 도로 노면 파손의 오탐을 감소시켰다. 그 결과 동일한 테스트용 데이터베이스를 통해 알고리즘의 인식 성능을 mAP 평균값 기준 9.44%만큼 향상시킨 새로운 방법을 소개하고자 한다.

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

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참고문헌

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