Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture
Concrete Crack Pixel Classification Using an Encoder Decoder Based Deep Learning Architecture
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DOI：
10.1007/978-3-030-33720-9_46
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
U. Billah;A. Tavakkoli;H. La
中科院分区：
文献类型：
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作者：
U. Billah;A. Tavakkoli;H. La
水下横梁、桥面等危险区域的民用基础设施检查，是一项危险的任务此外，劳动强度、时间等因素也影响基础设施的检测。最近的研究[11]表明，民用基础设施的自主检查可以消除人工检查产生的大部分问题。在本文中，我们解决的问题，检测混凝土表面的裂缝。大多数最近的裂缝检测技术使用深度架构。然而，如何有效地找到裂纹的准确位置一直是一个难题。因此，本文提出了一种深层结构，以确定裂缝的确切位置。我们的架构将每个像素标记为裂缝或非裂缝，这消除了使用当前文献中任何现有后处理技术的需要[5，11]。此外，获取足够的数据用于学习是混凝土缺陷检测的另一个挑战。根据之前的研究，只有10%的图像包含边缘像素（在我们的例子中是缺陷区域）[31]。我们提出了一个强大的数据增强技术，以减轻需要收集更多的裂纹图像样本。实验结果表明，与我们的方法，显着的准确性，可以得到非常少的数据样本。我们提出的方法也优于现有的混凝土裂缝分类方法。
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.