  Numerous detection problems in computer vision, including road crack
detection, suffer from exceedingly foreground-background imbalance.
Fortunately, modification of loss function appears to solve this puzzle once
and for all. In this paper, we propose a pixel-based adaptive weighted
cross-entropy loss in conjunction with Jaccard distance to facilitate
high-quality pixel-level road crack detection. Our work profoundly demonstrates
the influence of loss functions on detection outcomes, and sheds light on the
sophisticated consecutive improvements in the realm of crack detection.
Specifically, to verify the effectiveness of the proposed loss, we conduct
extensive experiments on four public databases, i.e., CrackForest, AigleRN,
Crack360, and BJN260. Compared with the vanilla weighted cross-entropy, the
proposed loss significantly speeds up the training process while retaining the
test accuracy.
