Learning-Based Defect Recognitions for Autonomous UAV Inspections
This work addresses crack inspection in concrete structures using UAVs, but it is incremental as it builds on existing methods and datasets.
The paper tackles crack detection and segmentation for autonomous UAV inspections by implementing deep learning frameworks based on classical architectures and proposing a hierarchical CNN for segmentation, achieving satisfactory results on a new benchmark dataset.
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