Potholes, one of the main causes of road-surface damage, pose a physical hazard to drivers, cause vehicle damage, and increase road maintenance costs. Hence, a model that enhances the accuracy of pothole detection and improves the real-time detection speed is required. A new model based on dilated convolutional neural networks was developed using a dataset that considers various lighting conditions, road conditions, and pothole sizes and shapes. Although the existing YOLOv5 model demonstrated high speed, it exhibited some false-positive pothole detections. In contrast, the proposed dilated convolutional neural network achieved both high accuracy and an appropriate inference speed, making it suitable for real-time detection. Compared with traditional models, the proposed model demonstrated efficiency in terms of model size and inference speed, indicating its potential suitability for systems performing real-time pothole detection when installed directly in vehicles.

ABSTRACT
1. 서론
2. 기존 문헌 고찰
    2.1. 센서 기반 포트홀 검지 기술
    2.2. 이미지 처리 기반 포트홀 감지 방법
    2.3. 모델 기반 포트홀 감지 방법
3. 방법론
    3.1. 확장 합성곱 신경망
    3.2. YOLO V50
4. 분석 결과
    4.1. 학습데이터셋
    4.2. 모델 검증 방법
    4.3. 도로노면 파손 식별 결과
5. 결론
REFERENCES

• 김형규(한국건설기술연구원 도로교통연구본부 수석연구원) | Kim Hyungkyu (Principal Researcher Korea Institute of Civil Engineering and Building Technology, 283, Goyangdae-Ro, Ilsnaseo-Gi, Goyang-Si, Gyenggi 10223, Korea) Corresponding author