Flexible road pavements deteriorate primarily due to traffic and adverse environmental conditions. Cracking is the most common deterioration mechanism; the surveying thereof is typically conducted manually using internationally defined classification standards. In South Africa, the use of high-definition video images has been introduced, which allows for safer road surveying. However, surveying is still a tedious manual process. Automation of the detection of defects such as cracks would allow for faster analysis of road networks and potentially reduce human bias and error. This study performs a comparison of six state-of-the-art convolutional neural network models for the purpose of crack detection. The models are pretrained on the ImageNet dataset, and fine-tuned using a new real-world binary crack dataset consisting of 14000 samples. The effects of dataset augmentation are also investigated. Of the six models trained, five achieved accuracy above 97%. The highest recorded accuracy was 98%, achieved by the ResNet and VGG16 models. The dataset is available at the following URL: https://zenodo.org/record/7795975

翻译：柔性路面的劣化主要由交通荷载和不利环境条件引起。裂缝是最常见的劣化机制，其调查通常依据国际定义的分类标准人工进行。在南非，已引入高清视频图像用于道路调查，这使道路检测更加安全。然而，调查过程仍是一项繁琐的人工操作。实现裂缝等缺陷检测的自动化，可加速路网分析，并可能减少人为偏差与错误。本研究对六种最先进的卷积神经网络模型进行了裂缝检测性能比较。这些模型在ImageNet数据集上预训练后，使用包含14000个样本的新的真实世界二元裂缝数据集进行微调。同时，还研究了数据集增强的影响。在训练的六个模型中，五个模型准确率超过97%，其中ResNet和VGG16模型达到最高准确率98%。该数据集可通过以下链接获取：https://zenodo.org/record/7795975