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Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks

Zhu, Qiuchen and Phung, Manh Duong and Quang, Ha (2019) Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks. In: Australasian Conference on Robotics and Automation 2019, 9-11 December 2019, Adelaide, Australia.

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Unmanned aerial vehicles (UAV) are expected to replace human in hazardous tasks of surface inspection due to their flexibility in operating space and capability of collecting high quality visual data. In this study, we propose enhanced hierarchical convolutional neural networks (HCNN) to detect cracks from image data collected by UAVs. Unlike traditional HCNN, here a set of branch networks is utilised to reduce the obscuration in the down-sampling process. Moreover, the feature preserving blocks combine the current and previous terms from the convolutional blocks to provide input to the loss functions in order to reduce the weight of resized images and thus minimise the information loss. Experiments on images of different crack datasets have been carried out to demonstrate the effectiveness of the proposed HCNN.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications
Electronics and Communications > Electronics and Computer Engineering
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr Manh Duong Phung
Date Deposited: 19 Dec 2019 03:17
Last Modified: 19 Dec 2019 03:17

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