Fast and efficient computing for deep learning-based defect detection models in lightweight devices

Alparslan Fişne (), Alperen Kalay () and Süleyman Eken ()
Additional contact information
Alparslan Fişne: Aselsan Inc.
Alperen Kalay: Aselsan Inc.
Süleyman Eken: Kocaeli University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 28, 5035-5050

Abstract: Abstract Defect anomaly detection is beneficial in the production cycle of various industries. It is widely used in areas such as metal surface and fabric industries. This paper focuses on deep learning-driven defect detection models using energy-efficient computing. We concentrate on a segmentation-based defect detection model for metal surface anomaly detection, while we deal with a deconvolution-based defect detection model for fabric defects in this work. We propose a depth-wise convolution structure for the segmentation-based visual defect detection model. In addition, we apply the optimizations supported by the inference engine to two models. The segmentation-based defect detection model inference is approximately 10 $$\times $$ × faster than the original. Furthermore, the real-time requirement is achieved in a lightweight vision processing unit (VPU) device with a power consumption of only 1.5 Watts for the fabric defect detection model. The practical values of this work are multifaceted, offering substantial benefits in terms of cost reduction, product quality, real-time processing, energy efficiency, and scalability. These advancements not only improve operational efficiency but also contribute to sustainability efforts and provide a competitive advantage in the industry.

Keywords: Defect detection; Energy efficiency; Deep learning; Edge computing (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02487-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02487-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02487-z

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().