Fracture is one of the main failure modes of engineering structures such as buildings and roads. Effective detection of surface cracks is significant for damage evaluation and structure maintenance. In recent years, the emergence and development of deep learning techniques have shown great potential to facilitate surface crack detection. Currently, most reported tasks were performed by a convolutional neural network (CNN), while the limitation of CNN may be improved by the transformer architecture introduced recently. In this study, we investigated nine promising models to evaluate their performance in pavement surface crack detection by model accuracy, computational complexity, and model stability. We created 711 images of 224 by 224 pixels with crack labels, selected an optimal loss function, compared the evaluation metrics of the validation dataset and test dataset, analyzed the data details, and checked the segmentation outcomes of each model. We find that transformer-based models generally are easier to converge during the training process and have higher accuracy, but usually exhibit more memory consumption and low processing efficiency. Among nine models, SwinUNet outperforms the other two transformers and shows the highest accuracy among nine models. The results should shed light on surface crack detection by various deep-learning models and provide a guideline for future applications in this field.

翻译：裂缝是建筑和道路等工程结构的主要失效模式之一。有效检测表面裂缝对于损伤评估和结构维护具有重要意义。近年来，深度学习技术的出现和发展在促进表面裂缝检测方面展现出巨大潜力。当前，大多数已报道的任务由卷积神经网络（CNN）执行，而CNN的局限性可通过近期引入的Transformer架构得到改善。本研究考察了九种有前景的模型，通过模型精度、计算复杂度和模型稳定性评估其在路面裂缝检测中的性能。我们创建了711张224×224像素的带有裂缝标签的图像，选取了最优损失函数，比较了验证数据集和测试数据集的评估指标，分析了数据细节，并检查了每个模型的分割结果。本研究发现，基于Transformer的模型通常在训练过程中更容易收敛且具有更高的精度，但通常表现出更大的内存消耗和较低的处理效率。在九种模型中，SwinUNet优于其他两种Transformer模型，且精度最高。研究结果应为基于各类深度学习模型的表面裂缝检测提供启示，并为该领域的未来应用提供指导。