Guo Lei, Xiong Fengguang, Cao Yaming, Xue Hongxin, Cui Lei, Han Xie
Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China.
Shanxi Province's Vision Information Processing and Intelligent Robot Engineering Research Center, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2024 Dec 29;25(1):146. doi: 10.3390/s25010146.
Automatic crack detection is challenging, owing to the complex and thin topologies, diversity, and background noises of cracks. Inspired by the wavelet theory, we present an instance normalization wavelet (INW) layer and embed the layer into the deep model for segmentation. The proposed layer employs prior knowledge in the wavelets to capture the crack features and filter the high-frequency noises simultaneously, accelerating the convergence of model training. Furthermore, instance normalization in our layer is utilized to mitigate the feature differences, boosting the generalization capability. In addition, a fusion layer is added to merge the information across the different layers. The comparison experiments and ablation studies demonstrate that the INW layer steadily enhances recognition and convergence performance on the DeepCrack dataset and CRACK500 dataset.
由于裂缝的拓扑结构复杂且细微、具有多样性以及存在背景噪声，自动裂缝检测具有挑战性。受小波理论启发，我们提出了一个实例归一化小波（INW）层，并将该层嵌入到用于分割的深度模型中。所提出的层利用小波中的先验知识来捕获裂缝特征并同时过滤高频噪声，加速模型训练的收敛。此外，我们层中的实例归一化用于减轻特征差异，提高泛化能力。此外，添加了一个融合层以合并不同层之间的信息。对比实验和消融研究表明，INW层在DeepCrack数据集和CRACK500数据集上稳定地提高了识别和收敛性能。
