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.

中文翻译：

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CrackGAN：使用基于生成对抗学习的部分准确地面真相的路面裂缝检测

全卷积网络是用于逐像素语义分割/检测的强大工具。然而，在使用部分准确的地面实况 (GT) 处理裂纹检测时会出现问题：网络可能很容易收敛到将所有像素视为背景 (BG) 的状态，并且仍然实现非常好的损失，称为“全黑”现象，由于准确的 GT 不可用和数据不平衡。为了解决这个问题，我们提出了仅裂纹补丁（CPO）监督生成对抗学习用于端到端训练，这迫使网络始终生成裂纹 GT 图像，同时通过馈送保留裂纹和 BG 图像翻译能力将较大尺寸的裂纹图像转换为非对称 U 形生成器，以克服“全黑”问题。所提出的方法使用四个裂纹数据集进行了验证；并在效率和准确性方面与最近发表的作品相比达到了最先进的性能。