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基于注意力机制和边界约束的矿产采矿应用的输送带偏差检测
Long Ma1, Jiaming Han1,2, Chong Dong1
1School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243002, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了一种改进的U-Net模型,用于检测输送带偏差,大大减少工业环境中的材料泄漏和设备磨损. 这种先进的方法提高了智能监控系统的准确性和稳定性.
科学领域:
- 工业自动化 工业自动化
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 输送带偏差在工业环境中导致材料泄漏和设备损坏.
- 准确检测皮带偏差对于运营效率和安全至关重要.
研究的目的:
- 开发一种改进的基于U-Net的方法,用于准确检测输送带偏差.
- 增强特征提取和融合,在复杂的工业环境中提供强大的性能.
主要方法:
- 使用了U-Net和ResNet34编码器来增强功能提取.
- 集成了一个多尺度自适应指导注意 (MASAG) 模块,用于特征融合.
- 嵌入边界损失和精细细分的边缘检测和RANSAC回归.
主要成果:
- 实现了99.77%的准确性,超过了基线U-Net (99.65%) 和其他方法,如YOLOv5s,UFLD,SEU-Net和DeepLabV3+.
- 在各种复杂的操作条件下表现出强大的稳定性和实时性能.
- 在智能监控系统的实际应用中得到验证的有效性.
结论:
- 提议的改进的U-Net方法有效地检测输送带偏差.
- 这种方法为采矿输送带中的智能监控系统提供了可靠的技术途径.
- 该方法显著提高了准确性和稳定性,解决了工业挑战.
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