Detecting Surface Cracks on Buildings Using Computer Vision: An Experimental Comparison of Digital Image Processing and Deep Learning

Yadhunath, Ramshankar; Srikanth, S; Sudheer, Arvind; Jyotsna, C.; Amudha, J.

doi:10.1007/978-981-16-1249-7_20

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Authors of paper "Detecting Surface Cracks on Buildings Using Computer Vision: An Experimental Comparison of Digital Image Processing and Deep Learning" [5] stated that the presence of cracks in concrete has a major impact on the durability of reinforced concrete since they represent an easy path to aggressive agents to reach the reinforcement and trigger the onset of corrosion. When performing damage assessment of infrastructures, a visual inspection provides an easy mean to detect damages, especially concrete cracks since they are apparent.…”

Section: Medical Diagnosticsmentioning

confidence: 99%

Method for detecting defects in direct metal deposition using a neural network

Pivkin,

Khodanovich,

Grigoriev

et al. 2024

Laser + Photonics for Advanced Manufacturing

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Research of literature dedicated to neural networks and artificial intelligence was made. The topicality of the research in this area was proven. Neural network models most using in computer vision were chosen. The possibility of using a method to detect defects during the process of direct metal deposition based on neural network application was proven. Three neural network models based on U-Net, ResUNet and VGG-16 architectures were trained. The best of three models was chosen. Goals for future researches were set.

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Section: Medical Diagnosticsmentioning

confidence: 99%

Method for detecting defects in direct metal deposition using a neural network

Pivkin,

Khodanovich,

Grigoriev

et al. 2024

Laser + Photonics for Advanced Manufacturing

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“…As a result, there is a need to raise the level of subjectivity, stability and effectiveness in assessing hazelnut quality. The digital image processing Approaches and artificial intelligence methods can play a significant role in this endeavor as the method offers the potential for high speed, non-destructive classification of hazelnut (Yadhunath et al, 2022). Machine vision systems are suitable for inspecting rigid and predefined objects.…”

Section: Introductionmentioning

confidence: 99%

Image Processing and Traditional Machine Learning Based Classification of Brown Marmorated Stink Bug (Halyomorpha Halys) Defected Hazelnut

GADALLA,

ÖZTEKİN

2023

Tekirdağ Ziraat Fakültesi Dergisi

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Quality control of hazelnuts is a major concern in many regions across the world, but particularly in Turkey as the world's largest hazelnut producer. Using image processing and deep learning techniques, this study intended to detect and classify healthy hazelnuts and hazelnuts infected with the Brown Marmorated Stink Bug. Infected hazelnut samples were collected from the 2021 production period by experts. A Guppy Pro CCD camera-based image acquisition system was used to capture hazelnut images. A total of 400 RGB hazelnut images were captured to train machine learning models. Image segmentation process was carried out to subtract hazelnut images from the background using the Thresholding technique. Moment features were extracted from RGB and l*a*b* spaces to be used to train traditional machine learning models. Furthermore, the most relevant and discriminative feature set was selected using the Boruta feature selection method. Traditional machine learning models including Random Forest, Support Vector Machine, Logistic Regression, Naive Bayes, and Decision Tree were trained twice, once with all features and another with the selected feature set only. The overall accuracy, statistical characteristics of the confusion matrix, and model training time were all calculated to evaluate and compare models performances. As a result, threshold value of 50 was determined from the gray level histogram and was able to separate hazelnut image from the background perfectly. Only seven moment features were identified as the most discriminative features out of 24 features. The SVM model with all feature vectors had the greatest classification accuracy of 98.75 %. When only the selected features were employed, the performance of Random Forest and Logistic Regression models improved to 97.5 and 96.25 %, respectively.

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Concrete surface crack detection system through OpenCV library

Tumrate,

Mishra

2024

International Conference on Recent Advances in Modeling and Simulations Techniques in Engineering and Science (Ramstes-2021)

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Detecting Surface Cracks on Buildings Using Computer Vision: An Experimental Comparison of Digital Image Processing and Deep Learning

Cited by 6 publications

References 21 publications

Method for detecting defects in direct metal deposition using a neural network

Method for detecting defects in direct metal deposition using a neural network

Image Processing and Traditional Machine Learning Based Classification of Brown Marmorated Stink Bug (Halyomorpha Halys) Defected Hazelnut

Concrete surface crack detection system through OpenCV library

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