基于改进的YOLOv5s和光谱技术的煤炭和甘的目标识别
Pengcheng Yan1,2,3, Xuyue Kan2, Heng Zhang2
1State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种改进的YOLOv5s神经网络,用于煤识别,显著减少检测时间并提高准确性. 改进后的模型实现了100%的回忆率和0.98的检测准确性,超过了以前的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 地质技术工程 地质技术工程
背景情况:
- 现有的煤识别方法的检测时间长,准确度低.
- 多光谱成像为改进材料识别提供了潜在的潜力.
研究的目的:
- 通过改进的深度学习,开发一个高效和准确的煤识别系统.
- 为了缩短检测时间,提高煤识别的准确性.
主要方法:
- 收集了煤炭的多光谱图像.
- 通过将GIou Loss替换为CIou Loss和NMS替换为DIou NMS,改善了YOLOv5s神经网络.
- 使用高斯过和非局部平均降噪的预处理数据.
- 训练并将改进的YOLOv5s模型与原来的YOLOv5s和SSD模型进行比较.
主要成果:
- 与原始YOLOv5s和SSD相比,改进的YOLOv5s模型显示了较低的损失值和接近1的召回率.
- 实现了最短的检测时间,100%的回忆率和最高的平均检测准确性 (0.98在测试组).
- 模型大小减少了0.8 MB,方便了硬件移植.
结论:
- 改进的YOLOv5s神经网络显著提高了煤探测的准确性,并减少了处理时间.
- CIou Loss 和 DIou NMS 能够有效地处理重叠和小目标.
- 开发的系统适用于需要高效和精确的煤识别的实际应用.
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