A Suitable Neural Network to Detect Textile Defects
نویسندگان
چکیده
25% of the total revenue earning is achieved from Textile exports for some countries like Bangladesh. It is thus important to produce defect free high quality garment products. Inspection processes done on fabric industries are mostly manual hence time consuming. To reduce error on identifying fabric defects requires automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect detector. A multi-layer neural network is determined that best classifies the specific problems. To feed neural network the digital fabric images taken by a digital camera and converts the RGB images are first converted into binary images by restoration process and local threshold techniques, then three different features are determined for the actual input to the neural network, which are the area of the defects, number of the objects in a image and finally the shape factor. The develop system is able to identify two very commonly defects such as Holes and Scratches and other types of minor defects. The developed system is very suitable for Least Developed Countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time.
منابع مشابه
Identification of Textile Defects Based on GLCM and Neural Networks
In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can poten...
متن کاملReal Time Automated Fabric Defect Detection System using Microcontroller
In a Least Developed Country (LDC) like Bangladesh where the textile is the main core of our economy; still there is a major drawback in this sector which is the defect detection of the fabric. In the manual fault detection system with highly trained inspectors, very less percentage of the defects are being detected in upon fabrics in the textile industries. But a real time automatic system can...
متن کاملArtificial Neural Network Techniques in Identifying Plain Woven Fabric Defects
Textile industry is one of the main sources of revenue generating industry. The price of fabrics is severely affected by the defects of fabrics that represent a major threat to the textile industry. In manual inspection a very small percentage of defects are detected with highly trained, experienced inspectors. An automatic defect detection system can increase the defect detection percentage. I...
متن کاملAn Advanced and Automated Neural Network based Textile Defect Detector
All textile industries aim to produce competitive fabrics. The competition enhancement depends mainly on productivity and quality of the fabrics produced by each industry. In the textile sector, there have been an enlarge amount of losses due to faulty fabrics. In the Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from textile export, most defects arisi...
متن کاملFabric defect inspection system using neural network
In a Least Developed Country (LDC) like Bangladesh where the textile is the main core of the economy, there is a major drawback in this sector which is the defect detection of the fabric. In the manual fault detection system with highly trained inspectors, very less percentage of the defects is being detected in upon fabrics in the textile industries. But a real time automatic system can increa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2006