  Detecting concrete surface damages is a vital task for maintaining the
structural health and reliability of highway bridges. Currently, most of these
tasks are conducted manually which could be cumbersome and time-consuming.
Recent rapid advancement in convolution neural network has enabled the
development of deep learning-based visual inspection techniques for detecting
multiple structural damages. However, most deep learning-based techniques are
built on two-stage, proposal-driven detectors and using less complex image
data, which is not promising to promote practical applications and integration
within intelligent autonomous inspection systems. In this study, a faster,
simpler single-stage detector is proposed based on YOLOv3 for detecting
multiple concrete damages of highway bridges. To realize this, a bridge damage
dataset consisting of field inspection images is prepared. These images have
large variations in damage appearance and monitoring scene and are labeled with
four types of concrete damages: crack, pop-out, spalling and exposed rebar. The
original YOLOv3 is further improved by introducing a novel transfer learning
method, Batch Renormalization and Focal Loss. The improved YOLOv3 is evaluated
in terms of average precision and speed. The results show that the improved
YOLOv3 has a detection accuracy of up to 80%. Its performance is about 13%
better than the original YOLOv3.
