Investigating Lightweight Transformer Models for Defect Detection

Authors

  • Hanyun Wang

DOI:

https://doi.org/10.54097/ajst.v7i3.12694

Keywords:

Image processing; Anomaly detection; Vision Transformer; Quantization; Pruning.

Abstract

In industrial production, product defect detection is vital for quality control. Traditional manual inspection is inefficient and error-prone. Deep learning, particularly in image processing, has enabled computer-based automated defect detection. This paper proposes a Visual Transformer-based model to overcome limitations in industrial anomaly detection. Leveraging pretrained Vision Transformer and Point Transformer models, it extracts features from RGB images and point cloud data. Multimodal feature fusion enhances anomaly perception, with residual connections mitigating feature loss. On the MVTec AD dataset, it achieves 96.3% AU PRO for anomaly detection and 99.3% Pixel ROCAUC for anomaly segmentation. To enable deployment on devices like Raspberry Pi, the paper introduces a lightweight model via post-training quantization and pruning. This results in a 28.52% inference speedup with only a 1.08% average detection accuracy drop, facilitating practical industrial applications on compact devices.

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Published

27-10-2023

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Section

Articles

How to Cite

Investigating Lightweight Transformer Models for Defect Detection. (2023). Academic Journal of Science and Technology, 7(3), 10-16. https://doi.org/10.54097/ajst.v7i3.12694

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