This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

Abstract
1. 서 론
2. 전체 시스템의 구성 및 구현
    2.1 시스템 개요
    2.2 파노라마 이미지 수집부 시스템의 구성
    2.3 휠 검출 및 카운팅 모 듈
    2.4 휠 이미지 처리 부
    2.5 픽셀 거리 연산 모 듈
    2.6 차종 분류부
3. 실험 환경 및 결과 분석
    3.1 실험 환경
    3.2 데이터셋 구성
    3.3 휠 검출 정확도 평가
    3.4 차종 분류 정확도 분석
    3.5 성능 비교 및 분석
4. 결론 및 향후 연구 방향
    4.1 결 론
    4.2 향후 연구 방향
후 기
References

• 임병철(Principal Researcher, AI Semiconductor R&D Center, Korea Automotive Technology Institute, Katech) | B. C. Yim Corresponding author
• 이경수(Researcher, AI Semiconductor R&D Center, Korea Automotive Technology Institute, Katech) | K. S. Lee
• 윤득선(Executive Principal Researcher, AI Semiconductor R&D Center, Korea Automotive Technology Institute, Katech) | D. S. Yun