A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
This work addresses bridge inspection automation for infrastructure maintenance, but it is incremental as it applies an existing method to a specific domain with minor modifications.
The paper tackled the problem of parsing multiclass bridge elements in inspection images by evaluating deep neural networks, finding that High-Resolution Net achieved a 92.67% mean F1-score and 86.33% mean IoU with data augmentation and a small training set of 130 images.
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.