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A platform for research: civil engineering, architecture and urbanism
Position‐Invariant Neural Network for Digital Pavement Crack Analysis
Abstract: This article presents an integrated neural network‐based crack imaging system to classify crack types of digital pavement images. This system includes three neural networks: (1) image‐based neural network, (2) histogram‐based neural network, and (3) proximity‐based neural network. These three neural networks were developed to classify various crack types based on the subimages (crack tiles) rather than crack pixels in digital pavement images. These spatial neural networks were trained using artificially generated data following the Federal Highway Administration (FHWA) guidelines. The optimal architecture of each neural network was determined based on the testing results from different sets of the number of hidden units, learning coefficients, and the number of training epochs. To validate the system, actual pavement pictures taken from pavements as well as the computer‐generated data were used. The proximity value is determined by computing relative distribution of crack tiles within the image. The proximity‐based neural network effectively searches the patterns of various crack types in both horizontal and vertical directions while maintaining its position invariance. The final result indicates that the proximity‐based neural network produced the best result with the accuracy of 95.2% despite its simplest neural network structure with the least computing requirement.
Position‐Invariant Neural Network for Digital Pavement Crack Analysis
Abstract: This article presents an integrated neural network‐based crack imaging system to classify crack types of digital pavement images. This system includes three neural networks: (1) image‐based neural network, (2) histogram‐based neural network, and (3) proximity‐based neural network. These three neural networks were developed to classify various crack types based on the subimages (crack tiles) rather than crack pixels in digital pavement images. These spatial neural networks were trained using artificially generated data following the Federal Highway Administration (FHWA) guidelines. The optimal architecture of each neural network was determined based on the testing results from different sets of the number of hidden units, learning coefficients, and the number of training epochs. To validate the system, actual pavement pictures taken from pavements as well as the computer‐generated data were used. The proximity value is determined by computing relative distribution of crack tiles within the image. The proximity‐based neural network effectively searches the patterns of various crack types in both horizontal and vertical directions while maintaining its position invariance. The final result indicates that the proximity‐based neural network produced the best result with the accuracy of 95.2% despite its simplest neural network structure with the least computing requirement.
Position‐Invariant Neural Network for Digital Pavement Crack Analysis
Lee, Byoung Jik (author) / Lee, Hosin “David” (author)
Computer‐Aided Civil and Infrastructure Engineering ; 19 ; 105-118
2004-03-01
14 pages
Article (Journal)
Electronic Resource
English
Position-Invariant Neural Network for Digital Pavement Crack Analysis
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