LGR-Net:一个针对电梯导轨压力板的轻量级缺陷检测网络.
Ruizhen Gao1,2,3, Meng Chen1, Yue Pan1
1School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
一个新的轻量级网络,LGR-Net,有效地检测电梯导轨压力板中的小缺陷. 它实现了高精度和回忆,降低了计算复杂性,为安全提供了有效的解决方案.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 电梯导轨压力板对于稳定性至关重要,但缺陷可能会危及安全.
- 现有的缺陷检测方法在准确性和效率方面扎,特别是在小缺陷方面.
研究的目的:
- 开发电梯导轨压力板的轻量级和准确的缺陷检测网络.
- 解决当前算法在定位小缺陷和计算负载方面的局限性.
主要方法:
- 拟议的LGR-Net是一个基于YOLOv8n的轻量级网络,结合了MobileNetV3和GhostConv.
- 通过P2层来增强网络,用于小物体检测和CBAM用于功能融合.
- 利用数据增强来创建一个定制的数据集用于培训和验证.
主要成果:
- 与其他YOLO系列模型相比,LGR-Net表现出更高的性能.
- 实现了高精度 (98.7%),回忆 (98.9%) 和mAP (99.4%).
- 显著减少参数数量 (2,412,118) 在保持高精度的同时.
结论:
- LGR-Net提供了一种高效和有效的解决方案,用于检测电梯导轨压力板的缺陷.
- 该网络提供了低计算复杂性和高检测精度的平衡.
- 这一进步有助于提高电梯系统的安全性和维护.
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