  Fully convolutional network is a powerful tool for per-pixel semantic
segmentation/detection. However, it is problematic when coping with crack
detection using partially accurate ground truths (GTs): the network may easily
converge to the status that treats all the pixels as background (BG) and still
achieves a very good loss, named "All Black" phenomenon, due to the
unavailability of accurate GTs and the data imbalance. To tackle this problem,
we propose crack-patch-only (CPO) supervised generative adversarial learning
for end-to-end training, which forces the network to always produce crack-GT
images while reserves both crack and BG-image translation abilities by feeding
a larger-size crack image into an asymmetric U-shape generator to overcome the
"All Black" issue. The proposed approach is validated using four crack
datasets; and achieves state-of-the-art performance comparing with that of the
recently published works in efficiency and accuracy.
