Fine Grained Recognition of Stamping Defects in Automotive Panels Based on Deep Learning

During the stamping process, various defects may occur due to various factors such as materials, molds, and processes, such as cracks, dents, wrinkles, etc. [...]it is of great practical significance to quickly and accurately detect and identify stamping defects in automotive panels. [...]applying d...

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Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2024-08 (E72), p.670-680
Main Authors: Wang, Shanglin, Co, Celso
Format: Article
Language:English
Subjects:
Accuracy
Automobile industry
Brain
Classification
Deep learning
Defects
Design
Efficiency
Flaw detection
Machine learning
Methods
Neural networks
Panels
Performance evaluation
Recognition
Researchers
Stamping
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Summary:During the stamping process, various defects may occur due to various factors such as materials, molds, and processes, such as cracks, dents, wrinkles, etc. [...]it is of great practical significance to quickly and accurately detect and identify stamping defects in automotive panels. [...]applying deep learning technology to fine-grained recognition of stamping defects in automotive panels is expected to overcome the limitations of traditional methods and improve detection efficiency and accuracy. [...]this article will summarize the research findings and look forward to future research directions. 2. [...]during the testing phase, researchers will use an independent test set to evaluate the performance of the model (Feng et al. 2021). [...]as a powerful machine learning technology, deep learning has demonstrated its unique advantages in multiple fields. [...]applying deep learning technology to fine-grained recognition of stamping defects in automotive panels has enormous potential (Nayak et al. 2021).
ISSN:1646-9895