A novel hybrid approach for crack detection

作者：

Highlights：

• A novel hybrid approach which integrates a Faster R-CNN for crack patch detection, a DCNN for crack orientation recognition, and a Bayesian algorithm for integration. It provides a novel framework to combine deep learning models and Bayesian analysis to address challenging vision problems where the deep learning approaches with simple end-to-end learning strategy might not be effective.

• A distinctive approach to apply Faster R-CNN for the challenging task of crack detection by training it to detect crack patches of suitable SNR, and a semi-automatic method to annotate crack patches of suitable scales to train a Faster R-CNN.

• A new Bayesian integration algorithm based on local spatial proximity, orientation consistency and alignment consistency to connect associated neighboring crack patches and suppress false detections, as well as an efficient algorithm to learn the optimal parameters.

摘要

•A novel hybrid approach which integrates a Faster R-CNN for crack patch detection, a DCNN for crack orientation recognition, and a Bayesian algorithm for integration. It provides a novel framework to combine deep learning models and Bayesian analysis to address challenging vision problems where the deep learning approaches with simple end-to-end learning strategy might not be effective.•A distinctive approach to apply Faster R-CNN for the challenging task of crack detection by training it to detect crack patches of suitable SNR, and a semi-automatic method to annotate crack patches of suitable scales to train a Faster R-CNN.•A new Bayesian integration algorithm based on local spatial proximity, orientation consistency and alignment consistency to connect associated neighboring crack patches and suppress false detections, as well as an efficient algorithm to learn the optimal parameters.

论文关键词：Crack detection,Defect detection,Object detection,Convolutional neural network,Faster R-CNN,Bayesian fusion

论文评审过程：Received 28 November 2019, Revised 18 April 2020, Accepted 23 May 2020, Available online 31 May 2020, Version of Record 6 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107474