一种更快,更轻的检测方法来检测煤矿带输送机中外来物体
Bingxin Luo1,2, Ziming Kou1,2, Cong Han1,2
1School of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
在煤炭输送机中检测异物对于安全至关重要. 改进的YOLOv5模型快速识别非煤炭项目,提高运营效率,并实现边缘计算应用.
科学领域:
- 工程 工程师 工程师 工程师
- 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:
- 矿业操作中的带式输送机经常遇到异物,造成带损坏和操作中断等风险.
- 有效检测这些外来物体对于保持输送系统的安全性和效率至关重要.
研究的目的:
- 开发一种快速,低参数的物体检测方法,用于带式输送机中的非煤炭外来物体.
- 为了提高检查机器人在具有挑战性的采矿环境中运行的实时处理能力.
主要方法:
- 为了培训和测试,创建了一个传送带上的异物数据集.
- 在图像预处理中应用了暗通道脱方法,以改善恶劣环境中的清晰度.
- 优化了YOLOv5架构,通过修改脊柱和子来实现轻量级但准确的物体检测网络.
主要成果:
- 改进的YOLOv5模型在异物数据集上实现了94.9%的检测准确度.
- 与YOLOv5s相比,新模型将参数减少了43.1%,推断时间减少了54.1%,计算负载减少了43.6%.
- 检测准确度提高了2.5%.
结论:
- 开发的轻量级YOLOv5模型显著提高了外来物体检测速度和准确性.
- 该方法适用于边缘计算设备,有助于带式输送机的安全和高效运行.
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