어텐션 매커니즘 기반 심층 컨볼루션 뉴럴 네트워크를 사용한 산업용 불량 칩 검사

다국어 초록 (Multilingual Abstract)

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 (Table of Contents)

• 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