A video intelligent detection method for urban underground drainage pipe based on improved convolutional neural network

doi:10.13474/j.cnki.11-2246.2021.320

Abstract

Abstract: Urban underground drainage pipeline is an important infrastructure in municipal engineering, but with the aging of the pipeline, different types of defects begin to appear, it is an important challenge that how to identify the defects in a timely and effective manner for municipal management. In this regard, this paper proposes an IM-CNN algorithm based on improved convolutional neural network at the use of latest deep learning to recognize the pipeline video images intelligently. This algorithm uses the framework of InceptionV3. At the meantime, we design an improved feature fusion strategy and a new objective function for pipeline recognition for characteristics that defects in different categories of pipeline images has the unbalance of different categories. Experiments based on the underground drainage pipeline data set show that the predictive recognition ability of the proposed algorithm is not only superior to the traditional machine learning algorithm but also superior to the existing algorithms that directly utilize the convolutional neural network.

Key words: underground drainage pipeline, deep learning, CNN, multi-class learning, image classification

CLC Number:

P258

PENG Shugang, WANG Dacheng, TAN Junhui, AO Xuanfeng, YU Jiaying. A video intelligent detection method for urban underground drainage pipe based on improved convolutional neural network[J]. Bulletin of Surveying and Mapping, 2021, 0(10): 132-135,167.

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