Real Time Fabric Defect Detection

Pakistan has a dynamic, vigorous and export oriented textile industry that has an overwhelming impact on economy. Textile is the largest industrial sector in Pakistan and generates the country?s highest export earnings of about 58% and contributes with 8.5% to GDP.However, total share of Pakistani e

Project Title

Real Time Fabric Defect Detection

Project Area of Specialization

Software Engineering

Project Summary

Pakistan has a dynamic, vigorous and export oriented textile industry that has an overwhelming impact on economy. Textile is the largest industrial sector in Pakistan and generates the country’s highest export earnings of about 58% and contributes with 8.5% to GDP.However, total share of Pakistani exports in the global textile and clothing market is less than 1%.

Quality is an important facet in the production line of the textile industry. Thus, fault detection in fabric quality control is an essential aspect for a high quality textile product.There are many factors that affect the final product on the production line of textile manufacturing, such as material quality, mechanical factors, dye type, yarn size, and human factors. In general, textile fabric defects refer to defects on the surface of the fabric. Fabric defects, most of which are caused by process problems and machine malfunctions. Defects will affect the quality of the final product, resulting in a great waste of all kinds of resources.Therefore, effective fabric defect detection is one of the key measures for modern fabric manufacturers to control cost and enhance product value and core competence.The defects that occur frequently on the fabric pattern limit the manufacturers who are able to recover only 45- 65% of their profits from the off-quality goods. Hence, the defect detection process in the textile industry needs to satisfy high expectations of nearly 100% detection accuracy.

Therefore, any other methods that are adopted should be able to perform real time defect detection with agility and accuracy. In many textile companies, the workers perform the fabric quality control process through human visual examination. As such, quality control is totally observer dependent, and it lacks uniformity. 

In modern textile manufacturing, automatic fabric defect is an important way to ensure the textile quality. For long, fabric defect detection is implemented by manual visual inspection which is inadequate and expensive in the meantime. Accordingly, automatic fabric defect detection is necessary for the textile industry to reduce cost and increase productivity.It is reported that the detection rate will be lower than 60 % while an experienced inspector detecting a batch of 2 m-width cloth at the speed of 30 m/s. Compared to manual fabric defects detection, the automatic detection systems are more effective with higher efficiency.

Therefore, the development of computer technology in recent years has contributed significantly to industrial advancement, particularly to industrial automation. These technological advancements have led to the automation of various processes such as design, production, and inspection in the textile industry. Therefore, in light of the aforementioned demerits, an automated inspection system is proposed that is more fast, efficient and reliable than the traditional human defect detection.

Project Objectives

The objective of this proposed product was to explore the application of Image processing methodology for reducing the defect percentage in fabric manufacturing sector. The proposed automated inspection system development needs the resistant and effective fabric defect detection algorithms. The main goal of this research works as follows:

• To examine weave on the fabric defects and related feature extraction
  methodologies
• To create a system that acquires images of the overall weave mark and reduce the
  noise by using various types of filtering methods
• To enhance the techniques or develop a new one, increasing the accuracy of the
  classification.

Project Implementation Method

The automated defect inspection system will be able to detect even the very fine defects in the fabrics by comparing the reference and test images. In this method, all the images used are in RGB scale with identical resolution.

First, image enhancement is done on every test image to ensure a better contrast image and thus facilitate defect detection.

Later, image registration ensures that all the test images are in proper alignment. After this step, image subtraction is done to crosscheck the input against the reference image to detect any type of defects.

If a positive rating is noted for the presence of defects, then edge detection is applied to both the reference and test images, to enable tracing even the finer details.

Finally, the Sylvester Matrix-Based Similarity Method (SMBSM) is used to identify the defects in the fabrics. The method proposed works with 2275 ms computational speed and 93.4% average accuracy

Benefits of the Project

The proposed system scope is to cover the fabric inspection in the textile manufacturing process to ensure that no error is left undetected and hence the end result would be a quality product. 

Fabric defect detection is the determination process of the location, type and size of the defects found on the fabric surface. Generally, human inspection is used for fabric defect detection. In comparison to the human based inspection the automated inspection system has the following advantage. The advantage of this process is listed as follows:

• Capable to inspect fabric in a continuous manner without pause.
• The speed and the reliability of inspection are increased by using the automated inspection system.
• Automated systems are able to provide detailed information about the defect.
• The automated inspection characteristics are more stable and are less affected by the external conditions.
• Provides instant correction of small defects usually neglected by human eye due to carelessness, optical illusion and small defects.

Technical Details of Final Deliverable

The Final deliverable Real Time Fabric Defect Detection System is inculded following Technical / Non Technical Items:

• Computer Tab
• Raspberry PI 4 , 8 GB Ram
• HD Cameras

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Manufacturing

Other Industries

Food , Security

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure, Responsible Consumption and Production

Required Resources

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