  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. As a result, the weights of resized images can be reduced to
minimise the information loss. Experiments on images of different crack
datasets have been carried out to demonstrate the effectiveness of proposed
HCNN.
