改进了YOLOv7网络模型用于选机器人,用于在煤炭中检测和异物
Dengjie Yang1,2, Changyun Miao2,3, Xianguo Li2,3
1School of Mechanical Engineering, Tiangong University, Tianjin 300387, China.
这项研究引入了改进的YOLOv7网络用于煤炭道检测,提高了选择机器人的准确性和速度. 新型号显著提高了精度和回忆力,以有效地去除异物.
科学领域:
- 计算机视觉和机器学习
- 矿业工程和自动化工程与自动化
背景情况:
- 煤炭生产受到和外来物质的阻碍,影响了热性能和设备.
- 现有的帮派选择机器人缺乏速度和识别精度,需要改进检测方法.
研究的目的:
- 开发一个增强的YOLOv7网络模型,用于准确和快速检测煤炭中的和异物.
- 通过先进的计算机视觉技术,提高帮派选择机器人的性能.
主要方法:
- 一个工业摄像头捕获了煤炭,带和外来物质的图像,创建了一个数据集.
- 通过减少的骨干,一个小物体检测层,上下文变压器网络 (COTN),DIoU损失和双路径注意力机制来增强YOLOv7网络,创建YOLOv71 + COTN模型.
- 在定制数据集上训练和评估YOLOv71 + COTN模型.
主要成果:
- 与原始YOLOv7.7.5相比,YOLOv71 + COTN模型的精度提高了3.97%,回忆率提高了4.4%,mAP0.5提高了4.5%.
- 改进后的模型在运行时显示了GPU内存消耗的减少.
- 该方法可以快速准确地检测和异物.
结论:
- 改进的YOLOv71 + COTN网络为检测煤炭和异物提供了卓越的性能.
- 这一进步支持在煤炭生产中开发更高效,更准确的选机器人.
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