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Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning

التفاصيل البيبلوغرافية
العنوان: Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
المؤلفون: Junchen Xiang, Haoyu Shi, Xueyu Huang, Daogui Chen
المصدر: Applied Sciences, Vol 13, Iss 8, p 5179 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: deep learning, faster R-CNN, graphite grade, multi-scale fusion, relation-aware global attention, filter response normalization (FRN), Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Graphite stone is widely used in various industries, including the refractory, battery making, steel making, expanded graphite, brake pads, casting coatings, and lubricants industries. In the mineral processing industry, an effective and accurate diagnostic method based on FRCNN-PGR is proposed and evaluated, which involves cutting images to expand the dataset, combining them using the faster R-CNN model with high and low feature layers, and adding a global attention mechanism, Relation-Aware Global Attention Network (RGA), to extract features of interest from both the space and channel. The proposed model outperforms the original faster R-CNN model with 80.21% mAP and 87.61% recall on the split graphite mine dataset.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/13/8/5179; https://doaj.org/toc/2076-3417
DOI: 10.3390/app13085179
URL الوصول: https://doaj.org/article/94edf080b5344cb49a577d5ae5c5408e
رقم الأكسشن: edsdoj.94edf080b5344cb49a577d5ae5c5408e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app13085179