TY  - JOUR
TI  - Detection of garment manufacturing defects using CFPNet and deep belief network: an image-based approach
AU  - Mathew, Dennise
AU  - Brintha, N.C.
T2  - Industria Textila
AB  - The demand for high-quality items and the quickly shifting economic landscape increase the importance of ready-made
garment manufacturers in providing the correct quality product. It is difficult work in the textile industry since the efficacy
and efficiency of automatic flaw identification determine the quality and cost of every textile surface. In the past, the
textile industry used manual human efforts to find flaws in the manufacturing of clothing. The main downsides of the
manual garment fault identification technique include lack of concentration, human tiredness, and time requirements.
Applications based on digital image processing and computer vision can overcome the aforementioned restrictions and
shortcomings. In this article, we use intelligent algorithms like Channel-wise Feature Pyramid Network (CFPNet) based
on deep learning-based techniques with Deep Belief Network (DBN) to monitor the quality and predict any occurrences
of manufacturing problems in clothing. The suggested algorithm is mostly utilised in the textile industry to find flaws in
clothing while estimating client needs based on the environment and the economy to react quickly and meet business
objectives. The performance evaluation was used to determine the 12 kinds of garment faults, which included holes,
excessive margins, stains, cracks, inappropriate stitch balancing, needle breaks, ink stains, torn clothing, drop stitches,
soil content, and broken clothing. The suggested model obtains a 95.85% stain defect detection rate, a 97.33%
defect-free garment recognition rate, and a 97.16% hole defect recognition rate.
DA  - 2025/04/24/
PY  - 2025
DO  - 10.35530/IT.076.02.2024140
DP  - DOI.org (Crossref)
VL  - 76
IS  - 02
SP  - 160
EP  - 170
SN  - 12225347
ST  - Detection of garment manufacturing defects using CFPNet and deep belief network
UR  - https://www.revistaindustriatextila.ro/images/2025/2/002%20DENNISE%20MATHEW%20INDUSTRIA%20TEXTILA%20no.2_2025.pdf
Y2  - 2025/04/26/14:10:59
ER  -