Identification of cracks is essential to assess the structural integrity of
concrete infrastructure. However, robust crack segmentation remains a
challenging task for computer vision systems due to the diverse appearance of
concrete surfaces, variable lighting and weather conditions, and the
overlapping of different defects. In particular recent data-driven methods
struggle with the limited availability of data, the fine-grained and
time-consuming nature of crack annotation, and face subsequent difficulty in
generalizing to out-of-distribution samples. In this work, we move past these
challenges in a two-fold way. We introduce a high-fidelity crack graphics
simulator based on fractals and a corresponding fully-annotated crack dataset.
We then complement the latter with a system that learns generalizable
representations from simulation, by leveraging both a pointwise mutual
information estimate along with adaptive instance normalization as inductive
biases. Finally, we empirically highlight how different design choices are
symbiotic in bridging the simulation to real gap, and ultimately demonstrate
that our introduced system can effectively handle real-world crack
segmentation.

鑑於混凝土基礎設施結構完整性評估的重要性，鑑定裂紋至關重要。然而，對於計算機視覺系統來說，強大的裂紋分割仍然是一項具有挑戰性的任務，原因在於混凝土表面的多樣外觀、不同的照明和天氣條件以及不同缺陷的重疊。本文提出了一種基於分形的高度保真度裂紋圖形模擬器以及相應的完全標註的裂紋數據集，並通過利用點對點互信息估計和自適應實例正規化作為歸納偏差，補充了後者的系統。最後，通過實證研究不同設計選擇在彌合模擬與現實差距方面的相互作用，最終證明我們引入的系統能夠有效處理現實世界的裂紋分割。

设计一种混合神经系统，用于从基于分形的模拟学习现实世界的裂缝分割

Designing a Hybrid Neural System to Learn Real-world Crack Segmentation  from Fractal-based Simulation

Recognition of defects in concrete infrastructure, especially in bridges, is
a costly and time consuming crucial first step in the assessment of the
structural integrity. Large variation in appearance of the concrete material,
changing illumination and weather conditions, a variety of possible surface
markings as well as the possibility for different types of defects to overlap,
make it a challenging real-world task. In this work we introduce the novel
COncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification
of five commonly appearing concrete defects. We investigate and compare two
reinforcement learning based meta-learning approaches, MetaQNN and efficient
neural architecture search, to find suitable convolutional neural network
architectures for this challenging multi-class multi-target task. We show that
learned architectures have fewer overall parameters in addition to yielding
better multi-target accuracy in comparison to popular neural architectures from
the literature evaluated in the context of our application.

本文介绍了新型 COncrete DEfect BRidge IMage 数据集 (CODEBRIM)，用于对五种常见混凝土缺陷进行多目标分类，研究和比较了两种基于强化学习的元学习方法（MetaQNN 和高效神经架构搜索），以寻找适合这个具有挑战性的多类别多目标任务的卷积神经网络结构。结果显示，所学习的架构在我们的应用上比文献中普遍采用的神经网络架构具有更少的参数，并产生更好的多目标准确性。