Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances.

翻译：摘要：自动路面裂缝检测是确保路面在服役期间功能性能的重要任务。受深度学习启发，编码器-解码器框架已成为裂缝检测的有力工具。然而，这类模型通常为开环系统，倾向于将细裂缝视为背景。同时，这些模型无法自动纠正预测中的错误，也无法适应环境变化以自动提取并检测细裂缝。为解决该问题，我们将闭环反馈嵌入神经网络，使模型能够基于生成对抗网络自主学会纠正错误。由此得到的模型称为CrackCLF，包含前端和后端，即分割网络与对抗网络。前端采用U形框架生成裂缝图，后端采用多尺度损失函数，用于修正标签与（前端生成的）裂缝图之间的高阶不一致性，从而解决开环系统问题。实验结果表明，所提CrackCLF在三个公共数据集上优于其他方法。此外，所提CLF可定义为即插即用模块，能够嵌入不同神经网络模型中提升其性能。