Computer Vision based welding defect detection using YOLOv3

Abdallah Amine Melakhsou (1, 2, 3, 4) , Mireille Batton-Hubert (1, 2, 3, 4) , Nicolas Casoetto (5)

1 Mines Saint-Étienne MSE - École des Mines de Saint-Étienne
2 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
3 FAYOL-ENSMSE - Institut Henri Fayol
4 FAYOL-ENSMSE - Département Génie mathématique et industriel
5 e.l.m. Leblanc SAS

In the industry of hot water tanks, welding quality plays an important role in the durability of the final product. Welds are often inspected visually by the operator, which tends to be time-consuming and prone to a high error rate. Machine learning and Deep learning offer solutions for the automation of this task. In this paper, we propose a system for welding anomalies detection from weld images. We begin by developing an image acquisition system and then a software based on the You Only Look Once v3 (YOLOv3) network. The results show that the model identifies and localizes welding anomalies with high accuracy and fast inference time.

Domaines: Modélisation et simulation

Abdallah Amine Melakhsou, Mireille Batton-Hubert, Nicolas Casoetto. Computer Vision based welding defect detection using YOLOv3. 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2022, Stuttgart, Germany. ⟨10.1109/ETFA52439.2022.9921603⟩. ⟨hal-03837669⟩