#Bulk image downloader crack torrent false positive crack#
The outstanding advantage of the proposed CNN-based crack detection is that it spares multifarious work from features preextraction and calculation compared to traditional methods.
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In this paper, a deep CNN is proposed to establish an image classifier for crack detection. The building of a database requires lots of human resources and computations, but the good news is that the existing well-annotated image databases (ImageNet, CIFIA-10 and CIFAR-100, MNIST ) and parallel computations using graphic processing units (GPU) have solved the problems. The CNNs need to be trained using large number of manually classified images. Compared to the ANNs, the CNNs learn image features using fewer parameters computations due to the partial connections, sharing weights, and pooling process between neurons. CNNs are deep learning algorithms developed from the ANNs, and they are highlighted in image classification and object recognition. To discard the extracting process of crack features, convolutional neural networks (CNNs) are imported to detect crack in images. However, the performance of this method relies on the extracted crack features, so the results of them have inevitably been affected by false feature extraction using IPTs. The artificial neural networks (ANNs) and Support Vector Machine (SVM) are typical ML algorithms, and they were adopted to detect concrete cracks, spalling, and other structural damages. The ML-based methods first extract crack features using the IPTs, then evaluate whether or not the extracted features indicate cracks. To improve the performance of image-based crack inspection methods, researchers turn to machine learning (ML) algorithms. Although the IPTs are effective to detect some specific images, their robustness is poor because the crack images taken from a concrete structure may be affected by factors such as light, shadows, and rusty and rough surfaces in real-world situations. To further improve its performance, general global transforms and edge detection detectors were applied, such as fast Haar transform (FHT), fast Fourier transform (FFT), Sobel, and Canny edge detectors.
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The simplest way to detect cracks from images is using the structural features, including histogram and threshold. The IPTs can not only recognize cracks from images but also measure the width and orientation of the recognized cracks. To overcome the drawbacks of human-based crack detection method, many image processing techniques (IPTs) are developed to detect concrete cracks, concrete spalling, and potholes and cracks in asphalt pavement. Although human-based crack detection method is an effective way to detect cracks, the detection results are subjective and vary from one to another because inspectors only make evaluation of current condition according to existing guidelines and their experiences. They assess the concrete structure through analysing position and width of cracks. Conventional human-based crack detection method relies on trained inspectors to find cracks on the surface of a concrete structure based on their expertise and years of experiences. IntroductionĬrack detection is one of the most important links of concrete structure maintenance, and it directly reflects how safe, durable, and applicable the concrete structure is. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice.
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The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. Crack detection is important for the inspection and evaluation during the maintenance of concrete structures.