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We have prepared another database of 100 images covering all four surfaces and coping all the four surfaces. We used it to examine the accuracy of our crack detection model and to identify the border between cracks and smooth surfaces as smooth surface. Furthermore, we examined the detection result of cracks on the curved surface such as a shower floor and a toilet pit. Finally, the data set was saved and publicly provided online.
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In addition, our CNN needs a large database to train for crack detection. Previously, experienced supervises annotated crack data for training the CNNs [ 18 ], but it is expensive and requires labors. To solve this problem, we use the information of cracks from the above prepared data to train the CNN, so the experiment results using the training data prepared in this paper can be used as crack data to train the CNN. Since, the prepared crack data are not enough, we have made a larger crack data by collecting thousands of cracks from different types of actual concrete surfaces. Furthermore, our CNN results show the crack detection result at the border of the concrete surface, that enables us to automatically detect cracks. Moreover, as our crack data are larger, our CNN can be trained for the cracks, instead of the squares of our ground truth data.
The advantages of our CNN crack detection method lie in that it can be used for both crack detection on concrete surface and crack data collection. Since the crack detection is made based on the background information of the concrete surface including the width and the direction of crack, it is less affected by the noise and the stains on the concrete surface, and it is also less affected by light conditions. This advantage has reduced the workload required for manual crack detection. Meanwhile, since the detected crack is embedded with its boundary, crack analysis is easy. Besides, our CNN also has an advantage in that it can produce the shape information of crack accurately, so the cracks can be measured precisely.
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