Machine vision for crack inspection of biscuits featuring pyramid detection scheme

Nashat, S.; Abdullah, Azizi; Abdullah, Mohd Zaid

doi:10.1016/j.jfoodeng.2013.08.006

Cited by 39 publications

(20 citation statements)

References 26 publications

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“…This led to the development of an autonomous crack quantification approach using the obtained crack map, which can be applied for detection of crack thickness. In the study by Nashat et al, 1 pyramid SVM after Wilk's λ analysis is used for crack inspection of biscuits, and the effectiveness of Hough-based features for crack detection is demonstrated. The capabilities of different approaches for crack classification are compared in Table 2.…”

Section: Classification and Detection Of Defectsmentioning

confidence: 99%

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Liu

Yan

Maruvanchery

et al. 2017

International Journal of Advanced Robotic Systems

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We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.

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Section: Classification and Detection Of Defectsmentioning

confidence: 99%

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Liu

Yan

Maruvanchery

et al. 2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…This input vector S i is mapped into a high dimensional space H by applying kernel trick. Guassian radial basis function (RBF), a common choice of kernel, is used in this study [28]. It can be defined as follows:

K (S_{i}, S_{j}) = exp (- \frac{{‖ S_{i} - S_{j} ‖}^{2}}{2 σ^{2}})

…”

Section: Data Dimension Reduction and Classificationmentioning

confidence: 99%

A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch

Han

Song

et al. 2014

Sensors

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Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures.

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“…Cho et al (2016) [ 7 ] investigated the effects of illumination and shooting distance on crack image recognition by examining cracks in images taken with a camera. Nashat et al (2014) [ 8 ] proposed a pyramid automatic crack detection scheme. Zhang et al (2014) [ 9 ] presented an automatic crack detection and classification methodology for subway tunnel safety monitoring.…”

Section: Introductionmentioning

confidence: 99%

Automatic crack detection method for loaded coal in vibration failure process

2017

PLoS ONE

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In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM). A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.

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Machine vision for crack inspection of biscuits featuring pyramid detection scheme

Cited by 39 publications

References 26 publications

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch

Automatic crack detection method for loaded coal in vibration failure process

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