Deep-Learning-Based Bughole Detection for Concrete Surface Image
Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-bas...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/8582963 |
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| _version_ | 1832558834193793024 |
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| author | Gang Yao Fujia Wei Yang Yang Yujia Sun |
| author_facet | Gang Yao Fujia Wei Yang Yang Yujia Sun |
| author_sort | Gang Yao |
| collection | DOAJ |
| description | Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface. |
| format | Article |
| id | doaj-art-e687db32cb234e10a169d60828dc5808 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-e687db32cb234e10a169d60828dc58082025-02-03T01:31:32ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/85829638582963Deep-Learning-Based Bughole Detection for Concrete Surface ImageGang Yao0Fujia Wei1Yang Yang2Yujia Sun3Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400044, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400044, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400044, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400044, ChinaBugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface.http://dx.doi.org/10.1155/2019/8582963 |
| spellingShingle | Gang Yao Fujia Wei Yang Yang Yujia Sun Deep-Learning-Based Bughole Detection for Concrete Surface Image Advances in Civil Engineering |
| title | Deep-Learning-Based Bughole Detection for Concrete Surface Image |
| title_full | Deep-Learning-Based Bughole Detection for Concrete Surface Image |
| title_fullStr | Deep-Learning-Based Bughole Detection for Concrete Surface Image |
| title_full_unstemmed | Deep-Learning-Based Bughole Detection for Concrete Surface Image |
| title_short | Deep-Learning-Based Bughole Detection for Concrete Surface Image |
| title_sort | deep learning based bughole detection for concrete surface image |
| url | http://dx.doi.org/10.1155/2019/8582963 |
| work_keys_str_mv | AT gangyao deeplearningbasedbugholedetectionforconcretesurfaceimage AT fujiawei deeplearningbasedbugholedetectionforconcretesurfaceimage AT yangyang deeplearningbasedbugholedetectionforconcretesurfaceimage AT yujiasun deeplearningbasedbugholedetectionforconcretesurfaceimage |