论文标题
在检查图像分析中，用于多类桥梁元素解析的深神经网络
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
论文作者
论文摘要
空中机器人（例如无人机）已被利用进行桥梁检查。可以通过板载摄像机收集具有可识别的结构元素和明显表面缺陷的检查图像，以提供有价值的信息以进行条件评估。本文旨在确定用于在检查图像中解析多类桥梁元素的合适的深神经网络（DNN）。一组广泛的定量评估以及定性示例表明，高分辨率净（HRNET）具有所需的能力。通过数据增强和130张图像的训练样本，预先训练的HRNET有效地转移到结构元件解析的任务中，并达到了92.67％的平均F1得分和86.33％的平均值。
Aerial robots such as drones have been leveraged to perform bridge inspections. Inspection images with both recognizable structural elements and apparent surface defects can be collected by onboard cameras to provide valuable information for the condition assessment. This article aims to determine a suitable deep neural network (DNN) for parsing multiclass bridge elements in inspection images. An extensive set of quantitative evaluations along with qualitative examples show that High-Resolution Net (HRNet) possesses the desired ability. With data augmentation and a training sample of 130 images, a pre-trained HRNet is efficiently transferred to the task of structural element parsing and has achieved a 92.67% mean F1-score and 86.33% mean IoU.
