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With the development of deep learning techniques, many researchers have started using neural network-based models for road damage detection. Most of these works use convolutional neural networks (CNNs) for pixel-level segmentation of road images. For example, Fan et al. [16] first used a CNN-based classification network to filter images containing cracks, after which the damages were extracted by traditional image processing methods of filtering with adaptive thresholding. On the other hand, Feng et al. [17] pre-processed the images to filter image noise, input them into two different crack segmentation models, and finally used the predicted results to synthesize the geometric parameters of the cracks calculated using the prediction results. Subsequently, Nguyen et al. [18] proposed a two-stage CNN network for low-resolution image detection and segmentation, which shortens the processing steps while increasing the efficiency of automated detection. Cheng et al. [19] proposed a computerized road crack detection method based on the structure of U-Net and introduced a function of distance transformation to assign pixel weights according to the actual segmentation minimum distance to assign pixel weights. Rill-García et al. [20], on the other hand, used VGG19 to replace the original backbone feature extraction network (VGG16) based on U-Net for improving the accuracy of road crack segmentation in the presence of incorrect annotations.

How To Uninstall Other Cracks In Windows 7

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To evaluate the visualization results of the models, five representative models of YOLOv4-Mobilenetv2, EfficientDet-D0, YOLOX-L, EfficientDet-D4, and Our Approach of lightweight or accurate models are provided, as shown in Figure 13. These examples were taken from images of the test set covering the significant road damage, including transverse and longitudinal linear cracks, alligator cracks, bumps, potholes and crosswalks, and lane line blur. It can be seen that our proposed method outperforms the other models in terms of both classification and confidence scores. Among them, for the lightweight models YOLOv4-Mobilenetv2 and EfficientDet-D0, which have similar parameters, there are more deficiencies in pavement damage detection, such as cracks, potholes, etc. In comparison to the representative models YOLO-X and EfficientDet-D4, which have higher accuracy, our proposed method not only has absolute advantages in terms of the number of parameters, but it also performs better for the classification of small-sized targets like potholes and transverse linear cracks.

Tennis courts 9 - 16: repair cracks in all court surfaces, paint where necessary. Temporary repair to provide use for another year approx. $7,000. Repairs should allow use for Spring/Summer 2004 season. Resurfacing required for continued, long-term use. To use Minor Maintenance Funds

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

Recently, with the development of machine learning classified as deep learning inspired by structure of the brain called artificial neural networks (ANN) [45], many algorithms have been proposed to perform object detection and image classification tasks. ANN is employed to solve many civil engineering problems [46,47,48,49,50]. Gao and Mosalam in [51] applied the transfer learning to detect damage images with structural method, and this method can reduce the computational cost by using the pre-trained neural network model. Meanwhile, the author needs to fine the neural network to perform the crack detection. Local patch information was employed to inspect crack information by convolutional neural networks (CNN) in [52]. In CrackNet [53], the algorithm improved pixel-perfect accuracy based on CNN by discarding pooling layers. In CrackNet-R [54], a recurrent neural network (RNN) is deployed to perform automatic crack detection on asphalt road. Cha et al. [55] adopted a sliding windows based on CNN to scan and detect road crack. Fan et al. in [56] proposed a structured prediction method to detect crack pixels with CNN. The small structured pixel images (27 27 pixels) was input into the neural network, which may generate overload for the computer memory. Ensemble network is proposed to perform crack detection and measure pavement cracks generated in road pavement [57]. Maeda et al. on [58] adopted object detection network architecture to detect crack images, and the network architecture can be transferred to a smartphone to perform road crack detection. Cha et al. used the Faster-RCNN to inspect road cracks [59]. Yang et al. in [60] adopted a fully convolutional network (FCN) to inspect road pavement cracks at pixel level, which can perform crack detection by end-to-end training. Li et al. in [61] employed the you-only-look-once v3 (YOLOv3)-Lite method to inspect the aircraft structures, and the depth wise separable convolution and feature pyramid were adopted to design the network architecture and joined the low- and high-resolution for crack detection. Jenkins et al. presented an encoder-decoder architecture to perform road crack detection, and the function of the encoder and decoder layers are used to reduce the size of input image to generate lower level feature maps, and obtain the resolution of the input data with up-sampling, respectively [62]. Tisuchiya et al. proposed a data augmentation method based on YOLOv3 to perform crack detection, which can increase the accuracy effectively [63]. 2ff7e9595c