Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture

Civil infrastructure inspection in hazardous areas such as underwater beams, bridge decks, etc., is a perilous task. In addition, other factors like labor intensity, time, etc. influence the inspection of infrastructures. Recent studies [11] represent that, an autonomous inspection of civil infrastructure can eradicate most of the problems stemming from manual inspection. In this paper, we address the problem of detecting cracks in the concrete surface. Most of the recent crack detection techniques use deep architecture. However, finding the exact location of crack efficiently has been a difficult problem recently. Therefore, a deep architecture is proposed in this paper, to identify the exact location of cracks. Our architecture labels each pixel as crack or non-crack, which eliminates the need for using any existing post-processing techniques in the current literature [5, 11]. Moreover, acquiring enough data for learning is another challenge in concrete defect detection. According to previous studies, only 10% of an image contains edge pixels (in our case defected areas) [31]. We proposed a robust data augmentation technique to alleviate the need for collecting more crack image samples. The experimental results show that, with our method, significant accuracy can be obtained with very less sample of data. Our proposed method also outperforms the existing methods of concrete crack classification.