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Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2021-03-24 , DOI: 10.1111/mice.12686
Cai Luo 1 , Leijian Yu 2 , Jiaxing Yan 3, 4 , Zhongwei Li 1 , Peng Ren 1 , Xiao Bai 5 , Erfu Yang 2 , Yonghong Liu 6
Affiliation  

Structural health assessments are essential for infrastructure. By using an autonomous panorama vision-based inspection system, the limitations of the human cost and safety factors of previously time-consuming tasks have been overcome. The main damage detection challenges to panorama images are (1) the lack of annotated panorama defect image data, (2) detection in high-resolution images, and (3) the inherent distortion disturbance for panorama images. In this paper, a new PAnoramic surface damage DEtection Network (PADENet) is presented to solve the challenges by (a) using an unmanned aerial vehicle to capture panoramic images and a distorted panoramic augmentation method to expand the panoramic dataset, (b) employing the proposed multiple projection methods to process high-resolution images, and (c) modifying the faster region-based convolutional neural network and training via transfer learning on VGG-16, which improves the precision for detecting multiple types of damage in distortion. The results show that the proposed method is optimal for surface damage detection.

中文翻译:

使用深度神经网络从 360° 全景图自动检测多个钢表面的损坏

结构健康评估对于基础设施至关重要。通过使用基于自主全景视觉的检查系统,克服了以前耗时任务的人力成本和安全因素的限制。全景图像的主要损伤检测挑战是 (1) 缺乏带注释的全景缺陷图像数据,(2) 高分辨率图像中的检测,以及 (3) 全景图像的固有失真干扰。在本文中,提出了一种新的全景表面损伤检测网络 (PADENet) 来解决这些挑战:(a) 使用无人机捕获全景图像并使用失真全景增强方法来扩展全景数据集,(b) 采用提出了多种投影方法来处理高分辨率图像,(c) 修改更快的基于区域的卷积神经网络并通过 VGG-16 上的迁移学习进行训练,这提高了检测失真中多种类型损坏的精度。结果表明,所提出的方法对于表面损伤检测是最佳的。
更新日期:2021-03-24
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