A novel hybrid approach for crack detection. (November 2020)

• Record Type: Journal Article
• Authors: Fang, Fen; Li, Liyuan; Gu, Ying; Zhu, Hongyuan; Lim, Joo-Hwee
• Is Part Of: Pattern recognition. Volume 107(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Crack detection -- Defect detection -- Object detection -- Convolutional neural network -- Faster R-CNN -- Bayesian fusion; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107474
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Vision-based crack detection is of crucial importance in various industries, and it is very challenging due to weak signals in noisy backgrounds. In this paper, we propose a novel hybrid approach for crack detection in raw images, which combines deep learning models and Bayesian probabilistic analysis for robust crack detection. First, we re-train a state-of-the-art object detector ( e.g. a Faster R-CNN) to detect crack patches of suitable SNR (signal-noise-ratio). We design a semi-automatic method to generate ground truths of crack patches along crack lines for training. To further improve the accuracy of crack detections over the whole image, we propose a Bayesian integration algorithm to suppress false detections. Specifically, we use a deep CNN to recognize the orientation of the crack segment in each detected patch. Then, a Bayesian probability is computed on the accumulated evidence from detected adjacent patches within a neighborhood based on spatial proximity, orientation consistency and alignment consistency. The patch which lacks local supports is suppressed as false detection. An algorithm to learn the parameters of Bayesian integration is also derived. Extensive experiments and evaluations are performed on a new comprehensive dataset of crack images. The results show that our approach outperforms the state-of-the-art baseline approach on deep CNN classifier. Ablation experiments are also conducted to show the effectiveness of proposed techniques.