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精细YOLO:基于特征聚合和规范化瓦瑟斯坦距离的简化X射线禁止物体检测网络

Yu-Tong Zhou1, Kai-Yang Cao1, De Li1

  • 1Department of Computer Science and Technology, Yanbian University, Yanji 133002, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括

这项研究介绍了Fine-YOLO,一种用于X射线安全成像的轻量级物体检测模型. 它以更少的参数实现了高精度,改善了复杂背景中小物体的检测.

关键词:
一些X射线图像.这是一个YOLO YOLO.轻量级的模型轻量级的模型.禁止物体检测检测被禁止的物体检测.

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相关概念视频

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...