DC Field | Value | Language |
---|---|---|
dc.contributor.author | Moon, Seongrok | - |
dc.contributor.author | Lee, Jun Hui | - |
dc.contributor.author | Kim, Kyung Soo | - |
dc.contributor.author | Park, Chan | - |
dc.contributor.author | Park, PooGyeon | - |
dc.date.accessioned | 2024-03-06T05:44:49Z | - |
dc.date.available | 2024-03-06T05:44:49Z | - |
dc.date.created | 2024-02-21 | - |
dc.date.issued | 2023-10-18 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/121476 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.relation.isPartOf | 23rd International Conference on Control, Automation and Systems, ICCAS 2023 | - |
dc.relation.isPartOf | International Conference on Control, Automation and Systems | - |
dc.title | Real-Time Steel Surface Defect Detection and Classification with Inference Acceleration | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 23rd International Conference on Control, Automation and Systems, ICCAS 2023, pp.994 - 998 | - |
dc.citation.conferenceDate | 2023-10-17 | - |
dc.citation.conferencePlace | KO | - |
dc.citation.endPage | 998 | - |
dc.citation.startPage | 994 | - |
dc.citation.title | 23rd International Conference on Control, Automation and Systems, ICCAS 2023 | - |
dc.contributor.affiliatedAuthor | Park, PooGyeon | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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