Author:
Chen Xiuxin,Ye Yang,Zhang Xue,Yu Chongchong
Abstract
Abstract
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.
Subject
General Physics and Astronomy
Reference15 articles.
1. ImageNet classification with deep convolutional neural networks[C];Krizhevsky,2012
2. Rich feature hierarchies for accurate object detection and semantic segmentation[C];Girshick,2014
3. Faster R-CNN: Towards real-time object detection with region proposal networks[J];Ren;IEEE Transactions on Pattern Analysis & Machine Intelli-gence,2015
4. You Only Look Once: Unified, Real-Time Object Detection[C];Redmon,2016
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献