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Abstract：
Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.
摘要：
技术提供了许多潜力，可用于提高基础设施的完整性和效率。裂缝是可能影响任何结构的完整性或可用性的主要问题之一。通常,使用手动检查方法会导致延误，从而使情况恶化。自动化裂缝检测对于关键基础设施的有效管理和检查已经变得非常必要。先前的裂缝检测研究采用了基于深度卷积神经网络（DCNN）的分类和定位模型。这项研究提出并比较了转移学习的DCNN作为分类模型和特征提取器来克服这一限制的裂缝检测的有效性。本文的主要目的是介绍表面裂纹检测的各种方法，并在3个不同的数据集上比较它们的性能。在这项工作中进行的实验有三个方面：最初，在三个公开可用的数据集上分析了12个转移学习的DCNN模型对裂缝检测的有效性：SDNET，CCIC和BSD。精度为53.40%，ResNet101在SDNET数据集上的性能优于其他模型。EfficientNetB0是BSD数据集上最准确（98.8%）的模型，ResNet50在CCIC数据集上表现更好，准确率为99.8%。其次,采用两种图像增强方法来增强图像，并在12个DCNN上进行学习，以提高SDNET数据集的性能。实验结果表明，增强后的图像显着提高了转移学习裂纹检测模型的准确性。此外，从DCNN的最后一个完全连接层提取的深层特征用于训练支持向量机（SVM）。深度特征与SVM的集成提高了所有DCNN数据集组合的检测精度，根据准确性分析，精度,召回，和F1得分。
