Qu Zhong, Cao Chong, Liu Ling, Zhou Dong-Yang
IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4890-4899. doi: 10.1109/TNNLS.2021.3062070. Epub 2022 Sep 1.
Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. Within this multiscale feature fusion module, the high-level features are introduced directly into the low-level features at different convolutional stages. Besides, deep supervision provides integrated direct supervision for convolutional feature fusion, which is helpful to improve model convergency and final performance of crack detection. Multiscale convolutional features learned at different convolution stages are fused together to robustly represent cracks, whose geometric structures are complicated and hardly captured by single-scale features. To demonstrate its superiority and generalizability, we evaluate the proposed network on three public crack data sets, respectively. Sufficient experimental results demonstrate that our method outperforms other state-of-the-art crack detection, edge detection, and image segmentation methods in terms of F1-score and mean IU.
自动裂缝检测对于高效且经济的道路养护至关重要。随着卷积神经网络（CNN）的迅猛发展，近期的裂缝检测方法大多基于CNN。在本文中，我们通过一个新颖的多尺度卷积特征融合模块，提出了一种用于裂缝检测的深度监督卷积神经网络。在这个多尺度特征融合模块中，高层特征在不同的卷积阶段被直接引入到低层特征中。此外，深度监督为卷积特征融合提供了集成的直接监督，这有助于提高模型的收敛性以及裂缝检测的最终性能。在不同卷积阶段学习到的多尺度卷积特征被融合在一起，以稳健地表示裂缝，裂缝的几何结构复杂，单尺度特征很难捕捉到。为了证明其优越性和通用性，我们分别在三个公开的裂缝数据集上对所提出的网络进行了评估。充分的实验结果表明，我们的方法在F1分数和平均交并比方面优于其他现有的裂缝检测、边缘检测和图像分割方法。
