Abstract
Culverts and sewer pipes are critical components of drainage systems, and their failure can lead to serious risks to public safety and the environment. In this thesis, we explore methods to improve automated defect segmentation in culverts and sewer pipes. Collecting and annotating data in this field is cumbersome and requires domain knowledge. Having a large dataset for structural defect detection is therefore not feasible. Our proposed methods are tested under conditions with limited annotated data to demonstrate applicability to real-world scenarios. Overall, this thesis proposes three methods to significantly enhance defect segmentation and handle data scarcity. This can be addressed either by enhancing the training data or by adjusting a models architecture. First, we evaluate preprocessing strategies, including traditional data augmentation and dynamic label injection. These techniques significantly improve segmentation performance, increasing both Intersection over Union (IoU) and F1 score. Second, we introduce FORTRESS, a novel architecture that combines depthwise separable convolutions, adaptive Kolmogorov-Arnold Networks (KAN), and multi-scale attention mechanisms. FORTRESS achieves state-of-the-art performance on the culvert sewer pipe defect dataset, while significantly reducing the number of trainable parameters, as well as its computational cost. Finally, we investigate few-shot semantic segmentation and its applicability to defect detection. Few-shot learning aims to train models with only limited data available. By employing a bidirectional prototypical network with attention mechanisms, the model achieves richer feature representations and achieves satisfactory results across evaluation metrics.
Abstract (translated)
涵洞和污水管是排水系统中的关键组成部分，它们的故障可能会导致严重的公共安全和环境风险。在这篇论文中，我们探讨了改进涵洞及污水管道缺陷自动分割方法的方法。在这一领域收集并标注数据既费时又需要专业知识，因此建立一个用于结构缺陷检测的大规模数据集是不现实的。我们的研究方法是在有限的标注数据条件下进行测试，以展示其应用于实际场景的可能性。 总的来说，本文提出了三种显著提升缺陷分割性能并在面对数据稀缺问题时有效应对的方法，这些方法可以通过增强训练数据或者调整模型架构来实现。首先，我们评估了预处理策略的效果，包括传统的数据扩充以及动态标签注入等技术，这些技术能够显著提高分割效果，增加了交并比（IoU）和F1分数的值。 其次，本文介绍了FORTRESS这一全新架构，它结合了深度可分离卷积、自适应科莫格罗夫-阿诺尔德网络(KAN)以及多尺度注意力机制。FORTRESS在涵洞污水管道缺陷数据集上取得了最先进的性能，同时显著减少了训练参数的数量及其计算成本。 最后，我们探讨了少量样本语义分割技术的应用性，并将其应用于缺陷检测领域中。少量样本学习的目标是使用仅有的有限数量的数据来训练模型。通过采用双向原型网络和注意力机制，该模型能够获取更为丰富的特征表示，并在评价指标上取得了令人满意的结果。
URL
https://arxiv.org/abs/2601.15366
