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首页> 外文会议>Workshop of European Group for Intelligent Computing in Engineering;International conference on advanced computing and applications >Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN
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Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN

机译：基于深度学习的R-CNN快速下水道检查自动缺陷检测技术的开发和改进

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摘要

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 preprocessing. 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.
机译：当前，目视检查技术，特别是闭路电视（CCTV），通常用于下水道检查。应用计算机视觉技术对CCTV图像进行自动解释，以识别管道缺陷。但是，传统的计算机视觉技术需要复杂的手工特征提取和大量的图像预处理。在这项研究中，开发了一种基于深度学习的方法，用于使用更快的基于区域的卷积神经网络（更快的R-CNN）检测下水道缺陷。从央视下水道检查录像中采集了3000张图像，其中85％用于培训和验证，15％用于测试。根据平均平均精度（mAP），丢失率，检测速度和训练时间对训练模型进行了训练和评估。实践证明，所提出的方法适用于以高mAP和低漏失率准确检测下水道缺陷。此外，通过研究数据集大小，初始化网络类型和训练模式以及网络超参数对模型性能的影响，改进了初始模型。改进的模型实现了83％的mAP和快速的检测速度。这项研究有可能解决建筑，工程和建筑（AEC）行业中类似的对象检测问题，并在设计深度学习模型时提供参考。

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