  Drone imagery is increasingly used in automated inspection for infrastructure
surface defects, especially in hazardous or unreachable environments. In
machine vision, the key to crack detection rests with robust and accurate
algorithms for image processing. To this end, this paper proposes a deep
learning approach using hierarchical convolutional neural networks with feature
preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for
image binarization. First, a set of branch networks is proposed, wherein the
output of previous convolutional blocks is half-sizedly concatenated to the
current ones to reduce the obscuration in the down-sampling stage taking into
account the overall information loss. Next, to extract the feature map
generated from the enhanced HCNN, a binary contrast-based autotuned
thresholding (CBAT) approach is developed at the post-processing step, where
patterns of interest are clustered within the probability map of the identified
features. The proposed technique is then applied to identify surface cracks on
the surface of roads, bridges or pavements. An extensive comparison with
existing techniques is conducted on various datasets and subject to a number of
evaluation criteria including the average F-measure (AF\b{eta}) introduced here
for dynamic quantification of the performance. Experiments on crack images,
including those captured by unmanned aerial vehicles inspecting a monorail
bridge. The proposed technique outperforms the existing methods on various
tested datasets especially for GAPs dataset with an increase of about 1.4% in
terms of AF\b{eta} while the mean percentage error drops by 2.2%. Such
performance demonstrates the merits of the proposed HCNNFP architecture for
surface defect inspection.
