Pavement Distress Detection Based on Transfer Learning
Authors:
- 1 November 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 435-439
Abstract
With the rapid development of highway construction in China, more and more attention has been paid to highway maintenance. The traditional manual detection and recognition methods cannot meet the needs of highway development, so the research of detection and recognition technology based on road image has become particularly important. In recent years, deep learning has shown very high performance in target detection. Based on transfer learning, this paper reuses part of the network of pavement crack detection based on Faster R-CNN to improve the performance of pavement distress detection.Keywords
This publication has 5 references indexed in Scilit:
- How to get pavement distress detection ready for deep learning? A systematic approach2017
- Road crack detection using deep convolutional neural network2016
- Deep Residual Learning for Image Recognition2016
- An artificial neural network approach for spatially extending road traffic monitoring measures2016
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009