成都理工大学学报（自然科学版）2025，Vol.52Issue(5)：888-899,12.DOI:10.12474/cdlgzrkx.2024101502
基于改进YOLOv5s实现岩石薄片中副矿物实时视觉识别与定位
Real-time visual recognition and accessory mineral localization in rock thin sections based on improved YOLOv5s
摘要
Abstract
In early research on mineral identification which used microscopes and were based on deep learning,the main focus was on the identification of major minerals with large grain sizes.As such,there is currently a dearth of studies pertaining to the identification of significant but smaller accessory minerals.In this paper,we propose a method for identifying zircon,an accessory mineral,in three principal rock classes using the improved YOLOv5s algorithm.The YOLOv5 framework is enhanced by the integration of the improved efficient channel attention and simplified spatial pyramid pooling-fast modules.After training the model using zircon data from thin sections,the obtained optimal training weight file is combined with libraries such as MSS(multiple screen shots)to achieve the real-time identification and distribution of zircon in rock samples.The results show that the proposed method improves the precision,recall rate,and mAP50 of the improved YOLOv5s model to 88%,82%,and 86%,respectively,compared with previous methods.Therefore,combined with a rich dataset,the improved YOLOv5s model proposed in this paper can achieve real-time and accurate identification of small minerals in thin sections.关键词
副矿物/锆石/深度学习/目标检测/改进YOLOv5s/C3ECA/SimSPPFKey words
accessory minerals/zircon/deep learning/object detection/improved YOLOv5s/C3EAC/SimSPPF分类
天文与地球科学引用本文复制引用
刘恒,王浩铮,冯林峰,张易,郭杰,鱼晟林,李炳春..基于改进YOLOv5s实现岩石薄片中副矿物实时视觉识别与定位[J].成都理工大学学报（自然科学版）,2025,52(5):888-899,12.基金项目
国家自然科学基金(42072225) （42072225）
韩国国家研究基金G-LAMP项目(RS-2024-00442775). （RS-2024-00442775）
