TY  - CONF
ID  - SisLab3850
UR  - https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/includes/files/pap104s1-file1.pdf
A1  - Zhu, Qiuchen
A1  - Phung, Manh Duong
A1  - Quang, Ha
Y1  - 2019/12/11/
N2  - 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.
TI  - Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks
M2  - Adelaide, Australia
AV  - public
T2  - Australasian Conference on Robotics and Automation 2019
ER  -