TELKOMNIKA (Telecommunication Computing Electronics and Control) · 2020 · 19 citations · 19 references
ComputingCivil EngineeringComputer ScienceComputer VisionMachine LearningPavement EngineeringFeature DetectionFeature ExtractionImage AnalysisImage ClassificationImage Feature EngineeringPattern RecognitionComputer Vision For InspectionCrack DetectionCrack Feature ExtractionGray-level Co-occurrence MatricesImage Feature ExtractionNeural Network ClassificationStatistical Image FeaturesVisual Pavement Inspection
The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.
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Recent advances in convolutional neural networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen et al. · Pattern Recognition · 2017 · 6.3K citations
Road crack detection using deep convolutional neural network
Lei Zhang, Fan Yang, Yimin D. Zhang et al. · 2016 · 1.5K citations