Fabric Defect Detection in Real World Manufacturing Using Deep Learning
importance 4/10
ai assisted
Industrial Vision Systems and Defect Detection textile manufacturing stage: research open-source model
Problem
Detecting fabric defects automatically in real production settings where datasets are noisy and both plain and printed fabrics occur.
Why it matters
Fabric defects cause substantial annual waste and losses, and manual inspection is slow.
What the AI did
A deep learning model was trained on an indigenous dataset from Chenab Textiles to detect defects across fabric types.
Results
not stated in source
Impact
Evidence supports a real-industry dataset and model development, but no accuracy figures are stated.
Record
| Venue | Information |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | M Quamer Nasim, Rafia Mumtaz, Muneer Ahmad, Arshad Ali |
| Citations | 53 (OpenAlex, at import time) |
| References | 22 |
| Model or method | not stated in source |
| Organization | Chenab Textiles |
| Primary source | https://doi.org/10.3390/info15080476 |
| Open access | gold |
| Keywords | fabric defect detection, deep learning, manufacturing, quality control, real-world dataset |
Importance rating
4
real but narrower advance
Abstract states defects cause substantial annual waste and losses in fabric production; a detector trained on real mill data addresses a genuine industrial need, though scope is one plant/segment.
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