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Computer Science > Computer Vision and Pattern Recognition
Title: Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
(Submitted on 29 Dec 2023 (v1), last revised 26 Apr 2024 (this version, v2))
Abstract: Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detection application in one of the components of the ultrasonic sensors produced at Robert Bosch's manufacturing plants. Our quantum-inspired T-CNN operates on a reduced model parameter space to substantially improve the training speed and performance of an equivalent CNN model without sacrificing accuracy. More specifically, we demonstrate how T-CNNs are able to reach the same performance as classical CNNs as measured by quality metrics, with up to fifteen times fewer parameters and 4% to 19% faster training times. Our results demonstrate that the T-CNN greatly outperforms the results of traditional human visual inspection, providing value in a current real application in manufacturing.
Submission history
From: Sukhbinder Singh [view email][v1] Fri, 29 Dec 2023 15:47:22 GMT (316kb,D)
[v2] Fri, 26 Apr 2024 16:07:25 GMT (316kb,D)
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