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Updated: May 20, 2025

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基于改进的Yolov8nn的轻量级煤矿输送带外来物体检测
Jierui Ling1, Zhibo Fu1, Xinpeng Yuan2
1School of Coal Engineering, Shanxi Datong University, Datong, 037000, China.
Scientific reports
|March 26, 2025
概括
一个新的轻量级算法通过提高速度和降低模型复杂性来改善输送带上的异物检测. 这种方法显著减少参数和计算负载,同时保持实时应用的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 传送带外来物体检测的深度学习模型面临着速度慢,参数数量高和计算需求的挑战.
- 现有的方法在实时处理和在工业环境中高效部署方面存在困难.
研究的目的:
- 开发一种轻量级和高效的输送带外来物体检测算法.
- 解决现有的深度学习模型在速度,参数数量和计算负载方面的局限性.
主要方法:
- 一个改进的Yolov8n模型,包括一个轻量级的StarNet骨干,C2f.-EIEM模块,大型可分离内核注意力 (LSKA),C2f_MLCA,以及一个检测-LSDECD头.
- 用MPDIoU取代CIoU损失,以提高边界框预测的准确性.
- 整合细节增强转换 (DEConv) 以提高识别稳定性.
主要成果:
- 模型参数减少了80%,计算负载减少了60.49%,存储大小减少了69.35%.
- 在保持召回率的同时,实现了1.9%的精度增加.
- 证明增强功能学习,专注于关键功能,并改善识别稳定性.
结论:
- 拟议的轻量级算法有效地克服了传输带外来物体检测传统方法的局限性.
- 优化的模型适用于实时应用,特别是采矿环境中的煤运输带.
- 显著减少模型大小和计算需求为高效部署铺平了道路.
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