YOLOv4를 이용한 차량파손 검출 모델 개선
Improving the Vehicle Damage Detection Model using YOLOv4
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초록
본 논문에서는 YOLOv4를 이용하여 차량의 부위별 파손현황을 검출하는 기법을 제안한다. 제안 알고리즘은 YOLOv4를통해 차량의 부위와 파손을 각각 학습시킨 후 검출되는 바운딩 박스의 좌표 정보들을 추출하여 파손과 차량부위의 포함관계를 판단하는 알고리즘을 적용시켜 부위별 파손현황을 도출한다. 또한 성능비교의 객관성을 위하여 동일분야의 VGGNet을 이용한 기법, 이미지 분할과 U-Net 모델을 이용한 기법, Weproove.AI 딥러닝 모델 등을 대조 모델로 포함한다. 이를 통하여제안 알고리즘의 성능을 비교, 평가하고 검출 모델의 개선 방안을 제안한다.
Abstract
This paper proposes techniques for detecting the damage status of each part of a vehicle using YOLOv4. The proposedalgorithm learns the parts and their damages of the vehicle through YOLOv4, extracts the coordinate information of thedetected bounding boxes, and applies the algorithm to determine the relationship between the damage and the vehiclepart to derive the damage status for each part. In addition, the technique using VGGNet, the technique using imagesegmentation and U-Net model, and Weproove.AI deep learning model, etc. are included for objectivity of performancecomparison. Through this, the performance of the proposed algorithm is compared and evaluated, and a method toimprove the detection model is proposed.
참고문헌 (11)
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