  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.
