An efficient scheme for the detection of defective parts in fabric images using image processing

Mahmood, Toqeer; Ashraf, Rehan; Faisal, C. M. Nadeem

doi:10.1080/00405000.2022.2105114

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Computer vision and digital image processing are used in various applied domains such as remote sensing, pose detection, decision making, path detection, defect detection, and automatic driving [21][22][23][24][25][26]. e recent focus of research in this field is the use of deep learning models that have shown good results in various applied domains [27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Waris

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.

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Section: Literature Reviewmentioning

confidence: 99%

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Waris

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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“…A survey indicates that the selling price of defective textile products may decrease by more than 45% [2]. Therefore, how to accurately identify fabric defects has become a key step in the textile industry that cannot be ignored [3,4].…”

Section: Introductionmentioning

confidence: 99%

Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm

Mei,

Shi,

Gao

et al. 2024

Electronics

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In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects.

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“…Second, the fabric contains some kind of defect on the fabric surface. e defective fabric is sold in 45% to 65% of the rst category, and it represents a major loss for any textile industry [1,5]. However, the quality of the fabric can be improved by applying the latest technologies during the manufacturing because customer expectations vary with the quality [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Ashraf

Ijaz

Asif

et al. 2022

Mathematical Problems in Engineering

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Convolution neural network (CNN) is one of the most popular machine learning techniques that is being used in many applications like image classification, image analysis, textile archives, object recognition, and many more. In the textile industry, the classification of defective and nondefective fabric is an essential and necessary step to control the quality of fabric. Traditionally, a user physically inspects and classifies the fabric, which is an ineffective and tedious activity. Therefore, it is desirable to have an automated system for detecting defects in the fabric. To address these issues, this research proposes a solution for classifying defective and nondefective fabric using deep learning-based framework. Therefore, in this research, an image processing technique with CNN-based GoogleNet is presented to classify defective and nondefective fabric. To achieve the purpose, the system is trained using different kinds of fabric defects. The performance of the proposed approach was evaluated on the textile texture TILDA dataset, and achieved a classification accuracy of 94.46%. The classification results show that the proposed approach for classifying defective and nondefective fabric is better as compared to other state-of-the-art approaches such as Bayesian, BPNN, and SVM.

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An efficient scheme for the detection of defective parts in fabric images using image processing

Cited by 7 publications

References 30 publications

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

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