Coal mine belt conveyor foreign object detectio
-
摘要: 针对现有基于深度学习的带式输送机异物检测方法存在检测速度慢的问题,提出了一种改进YOLOv3模型,并将其应用于煤矿带式输送机异物检测。该模型以轻量化网络DarkNet22-DS作为主干特征提取网络,DarkNet22-DS利用深度可分离卷积替换标准卷积,大幅减少了网络参数,并通过复合残差块提高了特征利用效率;通过引入加权双向特征金字塔网络及双尺度输出来改进特征融合网络,提升了模型对大块异物的检测效率;采用完全交并比损失函数作为目标框回归损失函数,充分利用目标框信息间的相关性,提高了模型的收敛速度和检测精度。将改进YOLOv3模型部署在嵌入式平台Jetson Xavier NX上进行煤矿带式输送机异物检测实验,结果表明,相较于YOLOv3模型,改进YOLOv3模型权重文件大小降低了91.4%,大幅减少了模型参数,检测速度提高了16倍,达30.7帧/s,满足煤矿带式输送机异物实时检测需求。Abstract: In order to solve the problem of slow detection speed of existing deep learning based belt conveyor foreign object detection methods, an improved YOLOv3 model is proposed and applied to coal mine belt conveyor foreign object detection. The model uses the lightweight network DarkNet22-DS as the backbone feature extraction network. DarkNet22-DS replaces the standard convolution with depthwise separable convolution, which reduces the network parameters significantly and improves the feature utilization efficiency by composite residual blocks. By introducing weighted bi-directional feature pyramid networks and dual-scale output, the model improves the feature fusion network and enhances the model's detection efficiency of large foreign objects. The complete intersection ratio loss function is used as the target box regression loss function, and the correlation between the target box information is fully utilized to improve the convergence speed and detection accuracy of the model. The improved YOLOv3 model is deployed on the embedded platform Jetson Xavier NX for coal mine belt conveyor foreign object detection experiments. The results show that compared with the YOLOv3 model, the weight file size of the improved YOLOv3 model is reduced by 91.4%, and the amount of model parameters is reduced significantly. The detection speed is increased by 16 times, reaching 30.7 frames/s. The performance meets the real-time detection requirements of foreign objects in coal mine belt conveyors.
-
-
期刊类型引用(16)
1. 张浪,刘彦青. 矿井智能通风与关键技术研究. 煤炭科学技术. 2024(01): 178-195 . 百度学术
2. 黄俊杰,杨应迪,黄建达. 矿井巷道火灾烟流流动规律及有效控制方案研究. 煤炭科技. 2024(01): 117-122+127 . 百度学术
3. 董翠翠. 基于LabVIEW的煤矿通风智能控制系统. 佳木斯大学学报(自然科学版). 2024(07): 77-80 . 百度学术
4. 李瑞文. 矿井通风机运行稳定性监测体系的应用研究. 西部探矿工程. 2024(12): 113-115 . 百度学术
5. 贾进章,尚文天,雷涛,李欣垚. 矿井智能通风发展趋势. 辽宁工程技术大学学报(自然科学版). 2024(05): 545-555 . 百度学术
6. 裴晓东,郝海清,王凯,蒋曙光,孙勇,陈佳辉,吴征艳,蒋合国,邵昊. 矿井复杂风网火灾风烟流应急调控技术及应用. 煤炭科学技术. 2023(05): 124-132 . 百度学术
7. 陈小建. 矿井通风机运行稳定性监测系统的应用分析. 机械管理开发. 2023(11): 208-209+212 . 百度学术
8. 郭玉柱. 斜沟煤矿火灾应急隔离系统设计研究. 煤. 2022(02): 13-16+29 . 百度学术
9. 苏君,吴昊,周琰. 变电站环境风机联动系统远程智能控制方法. 工业仪表与自动化装置. 2022(01): 20-24+51 . 百度学术
10. 杨旭,张浪,马强,刘彦青,张宏杰,赵凯凯,李伟,段思恭,耿锋. 多个采煤工作面风量按需动态联动调控系统. 工矿自动化. 2022(06): 112-117 . 本站查看
11. 郝海清,蒋曙光,王凯,吴征艳,裴晓东,邵昊. 基于Ventsim的矿井运输巷火灾风烟流应急调控技术. 煤矿安全. 2022(09): 38-46 . 百度学术
12. 吴新忠,张芝超,许嘉琳,王凯. 矿井智能风量调节研究. 工矿自动化. 2021(04): 44-50 . 本站查看
13. 郝海清,王凯,张春玉,蒋曙光,王海宽. 矿井皮带巷火灾风烟流场-区-网演化与调控规律. 中国矿业大学学报. 2021(04): 716-724 . 百度学术
14. 王凯,蔡炜垚,高士伟,陈新宇,张雨晨. 矿井火灾风烟流应急调控系统联动可靠性研究. 中国矿业大学学报. 2021(04): 744-754 . 百度学术
15. 李义宝. 基于实时监控系统的煤矿智能通风系统的研究. 山东煤炭科技. 2021(07): 211-213 . 百度学术
16. 程晓之,王凯,郝海清,陈瑞鼎,吴建宾. 矿井局部通风智能调控系统及关键技术研究. 工矿自动化. 2021(09): 18-24 . 本站查看
其他类型引用(7)
计量
- 文章访问数: 231
- HTML全文浏览量: 27
- PDF下载量: 39
- 被引次数: 23