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PS-YOLO-seg:一种基于改进的YOLOv12-seg的矿微观图像的轻量级实例细分方法

Zeyang Qiu^1,2, Xueyu Huang^1,3, Zhicheng Deng²

¹Yichun Lithium New Energy Industry Research Institute, Jiangxi University of Science and Technology, Yichun 336000, China.

Journal of imaging

|July 25, 2025

概括

这项研究介绍了PS-YOLO-seg,这是一种用于矿物分析的轻量级模型,用于改进智能采矿的微观图像识别. 它提供了增强的性能,减少了计算负载,非常适合边缘设备.

科学领域:

地质科学是地球科学.
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

微观图像识别对于矿物分析和智能采矿至关重要.
传统方法在边缘设备上部署时面临挑战.

研究的目的:

在可见光显微镜下开发用于矿石分析的轻量级细分模型.
解决传统方法的局限性,并使边缘设备部署成为可能.

主要方法:

建议PS-YOLO-seg,一个压缩的轻量级细分模型.
使用了宽度因子调整,基于PSConv的下方采样,C3k2-PS模块和Segment-Efficient头部.
在自建的矿石显微数据集上进行评估.

主要成果:

与YOLOv12n.相比,PS-YOLO-seg实现了略有改善的细分性能.
减少了20%的FLOP,减少了33%的参数数量,减少了32%的模型大小.
证明了更快的推断速度,适合实时部署.

结论:

架构优化显著提高了矿物的实例细分.
该模型是紧的,快速的,适合工业和边缘计算场景.

关键词:

YOLOv12-seg 的时间边缘部署边缘部署实例细分实例细分实例细分莱皮多利特 (Lepidolite) 是一种石头.轻量级网络轻量级的网络.微观成像显微镜成像

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