YOLO-STOD:一个基于Yolov5算法的工业输送带撕裂检测模型
Wei Liu1, Qing Tao2, Nini Wang3
1School of Mechanical Engineering, Xinjiang University, Urumqi, 830000, China.
Scientific reports
|January 10, 2025
概括
一种新的深度学习方法,YOLO-STOD,有效地实时检测输送带撕裂. 这种视觉检测系统通过在复杂干扰中改进小目标识别来提高煤炭行业的安全性.
科学领域:
- * 计算机视觉 计算机视觉
- * 深度学习 (Deep Learning) 是一种深度学习.
- * * 工业安全 工业安全
背景情况:
- *实时检测输送带撕裂对于煤炭行业的安全性和运营效率至关重要.
- *现有的方法难以应对输送带撕裂的多尺度性质,小目标和复杂干扰.
- * 需要改进的算法,能够在具有挑战性的工业条件下稳定检测小尺寸撕裂损伤.
研究的目的:
- * 开发一种基于深度学习的先进视觉检测方法,用于实时识别输送带撕裂.
- * 应对多尺度目标,大量小目标和撕裂检测复杂干扰的挑战.
- * 提高输送带撕裂检测系统的性能和稳定性,用于工业应用.
主要方法:
- * 开发一套专门的多种情况下的输送带撕裂数据集,以适应复杂的干扰和小尺寸检测场景.
- *设计了YOLO-STOD检测方法,结合了BotNet注意力机制,用于增强特征提取,特别是对于小目标.
- *使用Shape_IOU计算训练损失,考虑边界框形状回归来提高模型的稳定性.
主要成果:
- *在实验评估中,YOLO-STOD方法在与竞争方法相比,表现优越.
- * 实现了91.2%的回忆率,91.9%的地图值和190.966.6的每秒 (FPS).
- * 该方法有效地解决了小型目标和复杂干扰在输送带撕裂检测中的挑战.
结论:
- * 拟议的YOLO-STOD视觉检测方法对于实时检测输送带撕裂非常有效.
- * 系统的性能指标符合工业实时检测的严格要求.
- *YOLO-STOD是提高工业采矿部门安全性和操作监控的有希望的解决方案.
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