Earticle

초록

영어

The identification of anomalies in industrial settings poses a significant challenge, especially when there is a lack of negative samples and when the anomalous regions are small. Although existing computer vision methods have automated this task to some extent, these approaches struggle to extract salient features for inspecting defective chips. To tackle this problem, a deep learning-based framework is proposed for detecting anomalies in industrial settings. The framework utilizes a fine-tuned backbone convolutional neural network model and incorporates an enhanced attention mechanism. The attention module generates discriminative feature maps along two dimensions: channel and spatial. This is achieved by processing intermediate features obtained from the backbone model. These attention maps are then multiplied with the input feature map to dynamically enhance the relevant features. Extensive experiments demonstrate the effectiveness of our proposed method in maintaining a high level of detection accuracy for industrial product inspections. Consequently, our results conclude a suitable solution for optical chip inspection systems in industrial settings.

목차

Abstract
1. Introduction
2. Proposed Method
2.1 Proposed Features Optimizer and Extractor
3. Experimental Results
3.1. Dataset
3.2. Results comparison
4. Conclusions
Acknowledgment
References

키워드

저자

• Min Je Kim [ Sejong University Seoul, South Korea ]
• Altaf Hussain [ Sejong University Seoul, South Korea ]
• Muhammad Munsif [ Sejong University Seoul, South Korea ]
• Sangil Yoon [ Sejong University Seoul, South Korea ]
• Mi Young Lee [ Sejong University Seoul, South Korea ]
• Sung Wook Baik [ Sejong University Seoul, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004