Surface defect detection and Neural Network recognition of automotive body panels

Edris, Muhammad Zuhair Bolqiah; Jawad, Mohammed Saeed; Zakaria, Zahriladha

doi:10.1109/iccsce.2015.7482169

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Most deep-learning researches emphasize the importance of dataset amounts to neural network accuracy (Chen et al, 2017; Edris et al, 2015; Xia and Sattar, 2019), and the lack of labeling datasets may cause overfitting risks in training the deep-learning models. This implies that training the models effectively demand networks with deeper layers and vast amounts of structural parameters.…”

Section: Experiments and Evaluation For Dataset Augmentationmentioning

confidence: 99%

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

Lee

Lin

2021

Concurrent Engineering

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This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.

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Section: Experiments and Evaluation For Dataset Augmentationmentioning

confidence: 99%

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

Lee

Lin

2021

Concurrent Engineering

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“…35 A threshold is used to identify foreground objects, the value of which is determined using a variety of statistical parameters. Edris et al 36 presented a system for detecting surface defects on automobile body panels as part of quality control in industrial manufacturing. This paper presented a hybrid video compression approach based on foreground motion compensation.…”

Section: Introductionmentioning

confidence: 99%

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Rao,

Bansal,

Kaur

2023

Int J Imaging Syst Tech

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In the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

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“…Surface inspection and defect detection is a particular case in texture classification, where the algorithm attempts to inspect a surface for possible defects, to classify the input sample as either defective or non defective. Many vision-based inspection approaches have been developed to detect defects on textured surfaces with wide applications such as surface inspection of industrial products that include textile [7,14,20], metal surfaces [18], optical [3],tiles [2], motors [19],railway squats [12] etc. So, we have focused on the machine vision approach to the problem of defect detection of engineered surfaces.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

Srividhya¹,

Shanmugapriya²,

Priya³

2018

IJET

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In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.

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Surface defect detection and Neural Network recognition of automotive body panels

Cited by 11 publications

References 5 publications

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

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