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基于FDEP-YOLOv8的输送带损坏检测方法的研究
Yuan Yuan1, Yichao Bai2, Wenjun Meng1
1School of Vehicle and Traffic Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China.
本研究介绍了一种改进的YOLOv8算法,用于检测输送带损坏,提高工业环境中的安全性和效率. 新方法显著提高了确定皮带撕裂和断裂的准确性.
科学领域:
- 工业安全 工业安全 工业安全
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 输送带损坏在工业运营中构成重大安全风险.
- 现有的检测方法在复杂的背景,低效率和硬件限制方面扎.
研究的目的:
- 开发一个先进的输送带损坏检测算法.
- 克服当前检查技术的局限性,以提高安全性和效率.
主要方法:
- 一个改进的YOLOv8算法,包括一个焦点调制模块,动态上抽样 (Dysample) 和一个高效的多尺度注意力机制 (EMA).
- 利用PIoU v2损失函数来准确地对不规则的损伤形状进行边界框回归.
主要成果:
- 改进的YOLOv8模型实现了90.3%的精度和93.2%的平均精度 (mAP) 传输带损坏检测.
- 与原始YOLOv8和传统方法相比,证明了更高的检测准确性.
结论:
- 拟议的算法有效地提高了输送带损坏检测的准确性和效率.
- 这种先进的方法为工业安全监控提供了强大的解决方案.
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