Authors:
Xuban Barberena
;
Fátima A. Saiz
and
Iñigo Barandiaran
Affiliation:
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia, Spain
Keyword(s):
Quality Control, Deep Learning, Artificial Vision, Domain Adaptation, Model Drift, Defect Detection.
Abstract:
This study enhances industrial quality control by automating defect detection using artificial vision and deep learning techniques. It addresses the challenge of model drift, where variations in input data distribution affect performance. To tackle this, the paper proposes a simpler, practical approach to unsupervised Domain Adaptation (UDA) for object detection, focusing on industrial applicability. A technique based on the Faster R-CNN architecture and a Maximum Mean Discrepancy (MMD) regularization method for feature alignment is proposed. The study aims to detect data drift using state-of-the-art methods and evaluate the proposed UDA technique’s effectiveness in improving surface defect detection. Results show that statistical tests effectively identify variations, enabling timely adaptations. The proposed UDA method achieved mean Average Precision (mAP50) improvements of 3.1% and 6.1% under vibration and noise scenarios, respectively, and a significant 17.8% improvement for cond
itions with particles, advancing existing methods in the literature.
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