Bridge Damage Detection and Recognition Based on Deep Learning

Bridge damage detection is of vital importance to bridge safety. Nowadays the damage detection is mainly performed by human which is inefficient. We pro-posed a bridge damage detection and recognition method based on deep learning which is named DT-YOLOv3 in this paper. Our method is based on YOLOv3 object detection method and several improvements were made. First, deformable convolution was used to extract more accurate features, and transfer learning was introduced to improve the detection accuracy. Then, the model was compressed using group convolution and pruning. The test results show that our method is more effective than state-of-the-art methods and costs less time.