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논문 기본 정보

자료유형
학술대회자료
저자정보
Seongrok Moon (POSTECH) Jun Hui Lee (POSTECH) Kyung Soo Kim (POSTECH) Chan Park (POSTECH) PooGyeon Park (POSTECH)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2023
발행연도
2023.10
수록면
994 - 998 (5page)

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초록· 키워드

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Detecting defects on surfaces is a crucial challenge in the steel industry. Various object detection models, including one-stage and two-stage approaches, have been developed to address this problem. Currently, there is a scarcity of models that can effectively and efficiently handle both defect detection and classification tasks in real time. It is widely recognized that striking a balance between inference speed and the accuracy of object detection is a critical aspect that needs to be addressed. To address this challenge, our objective was to develop a model that ensures a high detection rate while achieving real-time processing capabilities. In pursuit of this objective, we conducted a comparative analysis between YOLOv7, a one-stage model, and Faster R-CNN, a two-stage model, followed by model optimization using TensorRT to enhance both inference speed and detection performance. As a result, we have successfully implemented a defect detection model utilizing actual production data, which achieved a detection rate of approximately 98.8% and a false ratio of 20%, while operating at a speed of 46 frames per second (FPS). This achievement demonstrates the effectiveness of our approach in balancing detection accuracy and inference speed.

목차

Abstract
1. INTRODUCTION
2. BACKGROUNDS
3. EXPERIMENTS
4. CONCLUSION
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