In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

1. 서 론
2. 연구방법
    2.1 능동소나(Active SONAR)
    2.2 데이터 획득
    2.3 이상 탐지
    2.4 오토 인코더
3. 연구결과
    3.1 관로 스캔 및 오토 인코더 모델 구축
    3.2 학습에 따른 측정결과
4. 결 론
사 사
References

• 김관태(주식회사 키네틱스 기술연구소) | Gwantae Kim (Research lab, Kinetix)
• 이준원(주식회사 키네틱스 기술연구소) | Junewon Lee (Research lab, Kinetix) Corresponding author