基于AI的涵洞-排水管检查
AI-Based Culvert-Sewer Inspection
AI总结：
本研究提出三种基于AI的方法，旨在提高涵洞和排水管道缺陷分割的精度，解决数据稀缺问题。
摘要：
涵洞和排水管道是排水系统的关键组成部分，其失效可能对公共安全和环境造成严重风险。在本论文中，我们探索了改进涵洞和排水管道自动缺陷分割的方法。在该领域收集和标注数据是繁琐的，需要专业知识。因此，拥有用于结构缺陷检测的大型数据集是不可行的。我们提出的方法是在标注数据有限的条件下进行测试，以证明其在现实场景中的适用性。总体而言，本论文提出了三种方法，显著提高缺陷分割并处理数据稀缺问题。这可以通过增强训练数据或调整模型架构来实现。首先，我们评估了预处理策略，包括传统数据增强和动态标签注入。这些技术显著提高了分割性能，提高了交并比（IoU）和F1分数。其次，我们引入了FORTRESS，一种新的架构，结合了深度可分离卷积、自适应Kolmogorov-Arnold网络（KAN）和多尺度注意力机制。FORTRESS在涵洞排水管缺陷数据集上实现了最先进的性能，同时显著减少了可训练参数的数量以及计算成本。最后，我们研究了少量样本语义分割及其在缺陷检测中的适用性。少量样本学习旨在仅用有限的数据训练模型。通过采用具有注意力机制的双向原型网络，模型实现了更丰富的特征表示，并在评估指标上取得了令人满意的成果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
