Image recognition method of building wall cracks based on feature distribution

In order to solve the building damage caused by cracks in the wall surface, based on feature distribution and SAR image segmentation technology, the cracks in the building wall were identified by extracting feature data images, de-noising and enhancing image edges, and segmenting target images. The validity and feasibility of the method are verified by the actual concrete wall image. The results show that the recognition accuracy of cracks in non-cracked walls is 100%, and that of longitudinal cracks is 78.2%. Compared with the color feature discrimination, this method has a good processing effect on the image, clear crack line, good coincidence degree with the original image, and crack width is close to the width of the original image. After processing the image with rough set, the recognition rate of the image is 98.1%, the false reject rate is 1.9%, the recognition time is 12 min, and the execution time of the algorithm is 126 s. After the processing of gray histogram, the feature distribution of image set has a certain distribution transfer, but the transfer effect is not particularly obvious. It can be found that this method has advantages of high recognition accuracy, short time, and practical application value, significantly enhancing pretreatment effect.

[1]  Xuehua Li,et al.  Numerical Investigation of the Effect of the Location of Critical Rock Block Fracture on Crack Evolution in a Gob-side Filling Wall , 2016, Rock Mechanics and Rock Engineering.

[2]  Bo Peng,et al.  Review on Automatic Pavement Crack Image Recognition Algorithms , 2015 .

[3]  Li Deng,et al.  Deep Dynamic Models for Learning Hidden Representations of Speech Features , 2014 .

[4]  Wen Gao,et al.  Mining Compact Bag-of-Patterns for Low Bit Rate Mobile Visual Search , 2014, IEEE Transactions on Image Processing.

[5]  Xin Yao,et al.  A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.

[6]  Guanghua Xu,et al.  Pre-Impact Fall Detection Based on a Modified Zero Moment Point Criterion Using Data From Kinect Sensors , 2018, IEEE Sensors Journal.

[7]  Laith Mohammad Abualigah,et al.  APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL , 2015 .

[8]  M. Remazeilles,et al.  Simulations for single-dish intensity mapping experiments , 2015, 1507.04561.

[9]  Ying Han,et al.  A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging , 2016, Neuroscience.

[10]  Laith Mohammad Abualigah,et al.  A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis , 2018, Eng. Appl. Artif. Intell..

[11]  Kin-Man Lam,et al.  Recognition of low-resolution face images using sparse coding of local features , 2016, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[12]  David Bersier,et al.  Bolometric light curves and explosion parameters of 38 stripped-envelope core-collapse supernovae , 2014, 1406.3667.

[13]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[14]  Ga-Won Lee,et al.  Novel silicon surface passivation by Al2O3/ZnO/Al2O3 films deposited by thermal atomic layer deposition , 2014 .

[15]  Hyuk-Jin Yoon,et al.  Analysis of Crack Image Recognition Characteristics in Concrete Structures Depending on the Illumination and Image Acquisition Distance through Outdoor Experiments , 2016, Sensors.

[16]  A. C. Fabian,et al.  Suzaku observations of Mrk 335: Confronting partial covering and relativistic reflection , 2014, 1410.2330.

[17]  David Eppstein,et al.  Ramified Rectilinear Polygons: Coordinatization by Dendrons , 2010, Discret. Comput. Geom..

[18]  Laith Mohammad Abualigah,et al.  Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering , 2018, Studies in Computational Intelligence.

[19]  M. Ebrahim Poorazizi,et al.  A Volunteered Geographic Information Framework to Enable Bottom-Up Disaster Management Platforms , 2015, ISPRS Int. J. Geo Inf..

[20]  Mu-Chun Su,et al.  A self organizing map optimization based image recognition and processing model for bridge crack inspection , 2017 .

[21]  FRANCIS FILBET,et al.  Asymptotically Stable Particle-In-Cell Methods for the Vlasov-Poisson System with a Strong External Magnetic Field , 2015, SIAM J. Numer. Anal..

[22]  Laith Mohammad Abualigah,et al.  Hybrid clustering analysis using improved krill herd algorithm , 2018, Applied Intelligence.

[23]  Bela Andras Racz,et al.  Geometry of (1,1)-Knots and Knot Floer Homology , 2015 .

[24]  A. H. Mazinan,et al.  An algorithm for extracting the phase of the fringe patterns with its applications to three-dimensional imaging through FPGA based implementation , 2016, 2016 International Conference on Industrial Informatics and Computer Systems (CIICS).

[25]  Laith Mohammad Abualigah,et al.  A new feature selection method to improve the document clustering using particle swarm optimization algorithm , 2017, J. Comput. Sci..

[26]  G. Schneider,et al.  Feature‐extraction from endopeptidase cleavage sites in mitochondrial targeting peptides , 1998 .