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Automatic Identification Technique for Siltation and Blockage Conditions in Drainage Pipes in Limestone Areas Based on Big Data and Transfer Learning
(1 CCCC Guanglian Expressway Investment Development Co., Ltd., Qingyuan 511518; 2 CCCC Fourth Harbor Engineering Research Institute Co., Ltd., Guangzhou 510220; 3 School of Civil and Transportation Engineering, Guangdong University of Technology,Guangzhou 510006)
Abstract Siltation, blockage or failure of tunnel drainage pipes could endanger the stability of slopes and the traf? fic safety of the roads. At present, there is a lack of research on the classification of siltation and blockage conditions in drainage pipes. To explore the intelligent detection method of siltation and blockage conditions in drainage pipes,this paper studies a transfer learning based convolutional neural network classification algorithm for siltation and blockage targets in drainage pipes, taking the Gaofeng tunnel on Guangzhou-Lianzhou Expressway and other tunnel projects in northern Guangdong as the background. By using a model transfer method, the drainage pipe image data are input into a pre-trained convolutional neural network for training in order to classify the new images. The collected image dataset of drainage pipes is tested to compare the identification accuracy of three different network models for the dataset. The results show that the classification and identification of siltation and blockage conditions in drainage pipes by using ResNet-18 could reach a 93% accuracy and achieve the effective classification of siltation and blockage conditions in drainage pipes. Also, the identification accuracy will be further improved with the expansion of the dataset in the future.
Abstract：
Siltation, blockage or failure of tunnel drainage pipes could endanger the stability of slopes and the traf? fic safety of the roads. At present, there is a lack of research on the classification of siltation and blockage conditions in drainage pipes. To explore the intelligent detection method of siltation and blockage conditions in drainage pipes,this paper studies a transfer learning based convolutional neural network classification algorithm for siltation and blockage targets in drainage pipes, taking the Gaofeng tunnel on Guangzhou-Lianzhou Expressway and other tunnel projects in northern Guangdong as the background. By using a model transfer method, the drainage pipe image data are input into a pre-trained convolutional neural network for training in order to classify the new images. The collected image dataset of drainage pipes is tested to compare the identification accuracy of three different network models for the dataset. The results show that the classification and identification of siltation and blockage conditions in drainage pipes by using ResNet-18 could reach a 93% accuracy and achieve the effective classification of siltation and blockage conditions in drainage pipes. Also, the identification accuracy will be further improved with the expansion of the dataset in the future.
LI Pengju1 ZHENG Fangkun2 LV Jianbing3 WU Weijun3 LIU Feng3 CHEN Gongfa3
.Automatic Identification Technique for Siltation and Blockage Conditions in Drainage Pipes in Limestone Areas Based on Big Data and Transfer Learning[J] MODERN TUNNELLING TECHNOLOGY, 2021,V58(4): 37-47
2021, Vol. 58 