Deep learning techniques have been studied and developed throughout the medical, agricultural, aviation, and automotive industries. It can be applied to construction fields such as concrete cracks and welding defects. One of the best performing techniques of deep running is CNN technique. CNN means convolutional neural network. In this study, we analyzed crack recognition of sewer with low recognition. Deep learning is generally more accurate with deeper layers, but analysis cost is high. In addition, many variations can occur depending on training options. Therefore, this study performed many parametric studies according to the variations of training options. When analyzed with appropriate training options, the accuracy was over 90% and stable results were obtained

1. 서 론
 2. CNN 개요
  2.1 특징 추출(Feature Extraction) 단계
  2.2 분류(Classification) 단계
 3. 하수관거 균열 인식을 위한 CNN
 4. 해석 예 및 결과 분석
 5. 요약 및 결론
 ACKNOWLEDGMENT
 REFERENCES

• 손병직(건양대학교 해외건설플랜트학과 교수) | Son, Byung-Jik
• 이규환(건양대학교 재난안전소방학과 교수) | Lee, Kyu-Hwan Corresponding author