scholarly journals Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Quanlei Wang ◽  
Ning Zhang ◽  
Kun Jiang ◽  
Chao Ma ◽  
Zhaochen Zhou ◽  
...  
Keyword(s):  
Edge Detection ◽  
Engineering Application ◽  
Objective Evaluation ◽  
Recognition Algorithm ◽  
Tunnel Lining ◽  
Automatic Identification ◽  
Main Body ◽  
Morphological Operations ◽  
Crack Recognition ◽  
Sobel Edge Detection

China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

scholarly journals Recognition of Tunnel Lining Cracks Based on Digital Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunquan Dai ◽  
Kun Jiang ◽  
Quanlei Wang
Keyword(s):  
Median Filter ◽  
Recognition Rate ◽  
Gaussian Function ◽  
Engineering Application ◽  
Recognition Algorithm ◽  
Safety Control ◽  
Computer Vision Technology ◽  
Tunnel Safety ◽  
Crack Recognition ◽  
Opening And Closing

Most of the tunnel projects are related to the national economy and people’s livelihood, and their operational safety is of paramount importance. Tunnel safety accidents or hidden safety hazards often start from subtleties. Therefore, the identification of tunnel cracks is a key part of tunnel safety control. The development of computer vision technology has made it possible for the automatic detection of tunnel cracks. Aiming at the problem of low recognition accuracy of existing crack recognition algorithms, this paper uses an improved homomorphic filtering algorithm to dehaze and clear the collected images according to the characteristics of tunnel images and uses an adaptive median filter to denoise the grayscaled image. The extended difference of Gaussian function is used for edge extraction, and the morphological opening and closing operations are used to remove noise. The breakpoints of the binary image are connected after removing the noise to make the image more in line with the actual situation. Aiming at the identification of tunnel crack types, the block index is proposed and used to distinguish linear cracks and network cracks. Using the histogram-like method to distinguish linear cracks in different directions can well solve the mixed crack situation in an image. Compared with the traditional method, the recognition rate of the new algorithm is increased to 94.5%, which is much higher than the traditional crack recognition algorithm. The average processing time of an image is 5.2 s which is moderate, and the crack type discrimination accuracy is more than 92%. In general, the new algorithm has good prospects for theoretical promotion and high engineering application value.

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

scholarly journals Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 265
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Fei Teng ◽  
Yunhao Lin ◽  
Mengjie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Hunan Province ◽  
Building Roof ◽  
Rural Buildings ◽  
High Resolution Images ◽  
Rgb Images ◽  
Sobel Edge Detection ◽  
Model Recognition

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Automatic Object Classification and Image Retrieval by Sobel Edge Detection

2014 ◽  
pp. 196-201
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Object Classification ◽  
Similarity Coefficient ◽  
Cosine Similarity ◽  
Retrieval Algorithms ◽  
Sobel Edge Detection ◽  
Classification Tool

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

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