A lightweight weld defect recognition algorithm based on convolutional neural networks

This paper proposes a lightweight weld defect-recognition algorithm based on a convolutional neural network that is appropriate for weld defect recognition in industrial welding. Specifically, the developed scheme relies on the original SqueezeNet model. However, we improve the fire module to reduce...

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Veröffentlicht in:	Pattern analysis and applications : PAA 2024-09, Vol.27 (3), Article 94
Hauptverfasser:	Zhao, Wenjie, Li, Dan, Xu, Feihu
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Burrs Computer Science Feature extraction Feature recognition Industrial and Commercial Application Lightweight Machine learning Modules Parameter identification Pattern Recognition Weight reduction Weld defects Welding parameters
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