Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN

Abstract

Currently, visual inspection techniques, especially closed-circuit television (CCTV), are commonly utilized for sewer pipe inspection. Computer vision techniques are applied for automated interpretation of CCTV images to identify pipe defects. However, conventional computer vision techniques require complex handcrafted feature extraction and large amount of image pre-processing. In this study, a deep learning based approach is developed for sewer pipe defect detection using faster region-based convolutional neural network (faster R-CNN). 3000 images were collected from CCTV inspection videos of sewer pipes, among which 85% were used for training and validation and 15% are for testing. The detection model was trained and evaluated in terms of mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with a high mAP and low missing rate. In addition, the initial model was improved by investigating the influence of dataset size, initialization network type and training mode, as well as network hyper-parameters on model performance. The improved model achieved a mAP of 83% and fast detection speed. This study has the potential for addressing similar object detection problems in the architecture, engineering and construction (AEC) industry and provides references when designing the deep learning models.