基于改进的YOLOv7微型目标检测算法进行的煤炭和河识别研究.
Yiping Sui1, Lei Zhang1,2, Zhipeng Sun1
1College of Coal Engineering, Shanxi Datong University, Datong 037003, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一种改进的YOLOv7微型模型,用于在具有挑战性的矿山环境中准确识别煤炭和. 改进后的模型实现了高精度和速度,这对于智能矿山建设至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 采矿工程 采矿工程 采矿工程
背景情况:
- 智能矿山建设依赖于精确的煤炭和道识别.
- 现有的模型在灰尘,低光矿场条件下,难以准确度低,目标小.
研究的目的:
- 为具有挑战性的采矿环境开发一个强大的煤炭和带识别模型.
- 为了提高小目标的识别精度和速度.
主要方法:
- 一个改进的YOLOv7微型算法,结合了协调注意力和上下文变压器模块.
- 功能金字塔网络模块的加权级联,以增强功能提取.
- 通过在煤矿环境中的实地测试进行验证.
主要成果:
- 改进的YOLOv7微型模型实现了97.54%的平均精度.
- 该模型的识别速度为每秒24.73.
- 在识别率和速度方面,超越了像Faster-RCNN,YOLOv3,YOLOv4和YOLOv5s这样的既定模型.
结论:
- 提议的增强型YOLOv7微型模型在煤炭和沟识别技术方面取得了重大进展.
- 这为智能矿山建设中准确识别提供了有效的解决方案.
- 该模型的性能在现实世界煤矿条件下得到验证.
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