ZHOUQian-qian,LIUHan-lin,CHENWei-feng,et al.Drainage Pipeline Defects Detection and Semantic Segmentation Based on Deeplabv3+[J].China Water & Wastewater,2022,38(13):22-27.
Drainage Pipeline Defects Detection and Semantic Segmentation Based on Deeplabv3+
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第38卷
Number:
第13期
Page:
22-27
Column:
Date of publication:
2022-07-01
- Keywords:
- pipeline defect; convolutional neural network; detection and location; semantic segmentation
- Abstract:
- The application of traditional computer vision technology in inspection and evaluation of CCTV sewer defects has many problems, such as incapable of multiple defects identification, poor detection effects and low detection accuracy, which cannot solve the tasks on multiple defects, real-time and accurate detection of drainage defects. In recent years, the emerging deep learning neural network technology has powerful data feature learning and processing capabilities. This paper proposes a pipeline defect detection and semantic segmentation method based on Deeplabv3+ convolutional neural network, which is used for multi-type detection, locating and geometric attribute segmentation of sewer defects. The impacts and mechanism of five types of backbone feature extraction networks, including ResNet-18, ResNet-50, Mobilenet_v2, Xception and InceptionResnet_v2, on defect detection and segmentation were analyzed respectively. The experimental results showed that the recognition and segmentation performance of ResNet-50 outperformed other networks. The accuracy rate was 89.8%, the mean and weighted mean intersection over union are 53.2% and 83.9%, respectively, and the segmentation rate is 12.50 frames/s. This method proposed can provide technical support and means for intelligent detection and segmentation of sewer defects.
Last Update:
2022-07-01