This study evaluated the effects of image preprocessing techniques on detection of Fire Department Connection (FDC) in road view images using a YOLOv8s-based framework. Six preprocessing techniques were applied under identical training and evaluation settings, and their performances were assessed using precision, recall, mAP@50, and mAP@50–95. Geometric correction produced the largest improvement, increasing mAP@50–95 from 0.419 to 0.543 and also improving recall, indicating enhanced localization and detection stability. HSV (Hue, Saturation, Value)-based red restoration achieved the highest average precision among color-based methods, whereas Retinex-based illumination correction degraded the performance across all metrics. Bottom-region cropping improved localization accuracy but reduced recall owing to limited spatial coverage. These results demonstrate that distortion mitigation and selective color enhancement are effective preprocessing strategies for robust FDC detection in road view environments. The study provides practical guidelines for intelligent road asset management, contributing to optimized road network operation and accessibility by reducing emergency vehicle positioning time in complex urban road environments.

ABSTRACT
1. 서론
    1.1. 논문개요
2. 선행연구 검토
    2.1. 소방활동설비 영상 기반 인식 연구 동향
    2.2. 거리/파노라마(어안) 영상 기반 객체 검출 연구
    2.3. 소형 객체 검출과 전처리 전략의 역할
    2.4. 선행연구의 한계 및 본 연구의 차별성
3. 연구방법
    3.1. 연구 방법 개요
    3.2. 객체 검출 모델 선정
    3.3. 로드뷰 이미지 수집
    3.4. 데이터 구성 및 학습–검증–시험 분할 설계
    3.5. 이미지 전처리 시나리오
    3.6. 실행 환경 및 평가조건
4. 성능 평가 결과
    4.1. 전처리 기법별 연결송수구 검출 성능 비교
    4.2. Baseline 모델 성능
    4.3. 기하 보정(평면화)의 효과
    4.4. 조명 보정(Retinex)의 영향
    4.5. 색상 기반 전처리의 효과
    4.6. 하단 영역 제거의 효과
    4.7. 결과 종합
5. 결론
REFERENCES

• 박형준(서울시립대학교 도시과학대학원 방재공학과 대학원생) | Park Hyeongjune
• 함승희(서울시립대학교 도시과학대학원 방재공학과 교수) | Haam Sunnie Corresponding author