Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed and will be established for the crack inspection tasks of various concrete structures. All our self-established datasets and codes are open-sourced at: https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection

翻译：自动裂缝检测与分割在无人机巡检系统中扮演着重要角色。本文基于Alexnet、VGG和Resnet等经典网络架构，实现了一种用于裂缝检测的深度学习框架。此外，受特征金字塔网络架构启发，本文还提出了一种高效的裂缝分割分层卷积神经网络（CNN）深度学习框架，并将其性能与其他前沿网络架构进行了对比。我们总结了现有裂缝检测与分割数据集，并建立了互联网上规模最大的裂缝检测与分割基准数据集，该数据集已向研究社区开源。我们在该基准数据集上测试了所提出的特征金字塔裂缝分割网络，并获得了令人满意的分割结果。同时，本文提出并即将构建一套自动无人机巡检框架，用于各类混凝土结构的裂缝检测任务。所有自主建立的数据集及代码均已开源，地址为：https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection