基于GSSA-YOLOM的外来物体和输送带偏差检测
Zuguo Chen1,2,3, Jiayu Liu2, Yimin Zhou4
1Sanya Institute of Hunan University of Science and Technology, Sanya 572024, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
一个新的GSSA-YOLOM算法通过检测异物和带偏差来提高煤矿中带式输送机的安全性. 这种人工智能模型提高了检测准确度,同时减少了可靠监控的计算负载.
科学领域:
- 工业安全 工业安全 工业安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 带式输送机的安全性在煤矿开采中至关重要.
- 当前的监控系统在同时检测多个问题时可能面临挑战.
研究的目的:
- 为带式输送机状态监控提出了一种新的多任务算法.
- 为了提高对外物体和皮带偏差的检测.
主要方法:
- 开发了一个GSSA-YOLOM算法,集成了用于多任务检测的细分头.
- 用于多尺度特征融合的非对称特征金字塔网络 (AFPN).
- 为了实现架构简化,采用了分组可分离的卷积 (GSConv) 和SlideLoss/Soft-NMS,以提高检测准确度.
主要成果:
- 与基线相比,GSSA-YOLOM模型在50%IoU (mAP@50) 的平均精度提高了3.4%.
- 将模型参数数量减少了27%,表明了计算效率.
- 证明有效检测异物和带偏差.
结论:
- GSSA-YOLOM算法为煤矿安全监控提供了一个强大的解决方案.
- 该模型平衡了检测精度与减少计算复杂性的平衡,适用于边缘设备.
- 这种方法满足了关键的煤矿安全监测要求.
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