Paper Defects Classification Based on VGG16 and Transfer Learning

Qu, Yun-hui; Tang, Wei; Feng, Bo

doi:10.7584/jktappi.2021.04.53.2.5

Cited by 5 publications

(3 citation statements)

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“…Various metrics were used in order to evaluate the performance of the proposed methodology. These include recall, precision, accuracy, and F1 measure [22]. In the proposed model, the idea is to freeze the top twelve layers and unfreeze the remaining layers to retrain the unfrozen layers.…”

Section: Resultsmentioning

confidence: 99%

Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System

Latif,

Brahim,

Abdelhamid

et al. 2023

ASI

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Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).

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Section: Resultsmentioning

confidence: 99%

Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System

Latif,

Brahim,

Abdelhamid

et al. 2023

ASI

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“…Among machine learning models used for image data classification, popular and highperformance models include VGG16 and ResNet50 [16][17][18][19][20][21]. VGG16 is a model composed of a total of 16 convolutional layers, pooling layers, and fully connected layers, making it widely used in image recognition and classification research [16].…”

Section: Related Workmentioning

confidence: 99%

“…VGG16 is a model composed of a total of 16 convolutional layers, pooling layers, and fully connected layers, making it widely used in image recognition and classification research [16]. In their research using VGG16, Qu et al proposed a method for detecting defects in paper using VGG16, particularly focusing on paper data with a small sample size [17]. To overcome the issue of overfitting, especially when dealing with a small dataset, the authors froze the first seven layers of VGG16 and fine-tuned the remaining convolutional layers using paper defect images.…”

Section: Related Workmentioning

confidence: 99%

Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram

Lee,

Choi,

Kim

2024

Electronics

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3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points’ heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as ‘good’ or ‘bad’ using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of ‘good’ and ‘bad’ by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models.

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