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基于YOLOv5s−FSW模型的选煤厂煤矸检测研究

燕碧娟 王凯民 郭鹏程 郑馨旭 董浩 刘勇

燕碧娟,王凯民,郭鹏程,等. 基于YOLOv5s−FSW模型的选煤厂煤矸检测研究[J]. 工矿自动化,2024,50(5):36-43, 66.  doi: 10.13272/j.issn.1671-251x.2023100090
引用本文: 燕碧娟,王凯民,郭鹏程,等. 基于YOLOv5s−FSW模型的选煤厂煤矸检测研究[J]. 工矿自动化,2024,50(5):36-43, 66.  doi: 10.13272/j.issn.1671-251x.2023100090
YAN Bijuan, WANG Kaimin, GUO Pengcheng, et al. Research on coal gangue detection in coal preparation plant based on YOLOv5s-FSW model[J]. Journal of Mine Automation,2024,50(5):36-43, 66.  doi: 10.13272/j.issn.1671-251x.2023100090
Citation: YAN Bijuan, WANG Kaimin, GUO Pengcheng, et al. Research on coal gangue detection in coal preparation plant based on YOLOv5s-FSW model[J]. Journal of Mine Automation,2024,50(5):36-43, 66.  doi: 10.13272/j.issn.1671-251x.2023100090

基于YOLOv5s−FSW模型的选煤厂煤矸检测研究

doi: 10.13272/j.issn.1671-251x.2023100090
基金项目: 山西省重点研发计划项目(202102010101010)。
详细信息
    作者简介:

    燕碧娟(1975—),女,山西芮城人,教授,博士,主要研究方向为智能矿山视觉感知关键技术,E-mail:tyustybj@tyust.edu.cn

  • 中图分类号: TD948.9

Research on coal gangue detection in coal preparation plant based on YOLOv5s-FSW model

  • 摘要: 针对现有煤矸检测模型存在的特征提取不充分、参数量大、检测精度低且实时性差等问题,提出了一种基于YOLOv5s−FSW模型的选煤厂煤矸检测方法。该模型在YOLOv5s的基础上进行改进,首先将主干网络的C3模块替换为FasterNet Block结构,通过降低模型的参数量和计算量提高检测速度;然后,在颈部网络引入无参型SimAM注意力机制,增强模型对复杂环境下重要目标的关注,进一步提高模型的特征提取能力;最后,在输出端用Wise−IoU替换CIoU边界框损失函数,使模型聚焦普通质量锚框,提高收敛速度和边框的检测精度。消融实验结果表明:与YOLOv5s模型相比,YOLOv5s−FSW模型的平均精度均值(mAP)提高了1.9%,模型权重减少了0.6 MiB,参数量减少了4.7%,检测速度提高了19.3%。对比实验结果表明:YOLOv5s−FSW模型的mAP达95.8%,较YOLOv5s−CBC,YOLOv5s−ASA,YOLOv5s−SDE模型分别提高了1.1%,1.5%和1.2%,较YOLOv5m,YOLOv6s模型分别提高了0.3%,0.6%;检测速度达36.4帧/s,较YOLOv5s−CBC,YOLOv5s−ASA模型分别提高了28.2%和20.5%,较YOLOv5m,YOLOv6s,YOLOv7模型分别提高了16.3%,15.2%,45.0%。热力图可视化实验结果表明:YOLOv5s−FSW模型对煤矸目标特征区域更加敏感且关注度更高。检测实验结果表明:在环境昏暗、图像模糊、目标相互遮挡的复杂场景下,YOLOv5s−FSW模型对煤矸目标检测的置信度得分高于YOLOv5s模型,且有效避免了误检和漏检现象的发生。

     

  • 图  1  YOLOv5s−FSW网络结构

    Figure  1.  YOLOv5s-FSW network structure

    图  2  FasterNet Block结构

    Figure  2.  FasterNet Block structure

    图  3  SimAM注意力机制

    Figure  3.  SimAM attention mechanism

    图  4  数据集示例

    Figure  4.  Dataset example

    图  5  模型改进前后煤矸热力图结果对比

    Figure  5.  Comparison of thermal map results of coal-gangue before and after model improvement

    图  6  模型改进前后煤矸检测效果对比

    Figure  6.  Comparison of coal-gangue detection results before and after model improvement

    表  1  消融实验结果

    Table  1.   Ablation experiment results

    模型 精确率/% 召回率/% mAP/% 权重/MiB 计算量 参数量/105 检测速度/(帧·s−1
    YOLOv5s 89.9 88.6 93.9 13.7 15.8 70.2 30.5
    改进模型1 89.6 85.1 93.5 13.1 14.3 66.9 37.8
    改进模型2 89.9 89.1 94.2 13.7 15.8 70.2 29.1
    改进模型3 91.1 89.7 95.3 13.7 15.8 70.2 28.3
    YOLOv5s−FSW 91.8 90.1 95.8 13.1 14.3 66.9 36.4
    下载: 导出CSV

    表  2  不同检测模型性能对比

    Table  2.   Performance comparison of different detection models

    模型 mAP/% 权重/MiB 计算量 检测速度/(帧·s−1
    YOLOv5s−CBC 94.7 15.3 15.9 28.4
    YOLOv5s−ASA 94.3 13.4 15.6 30.2
    YOLOv5s−SDE 94.6 12.7 12.1 37.8
    YOLOv5s 93.9 13.7 15.8 30.5
    YOLOv5m 95.5 40.2 47.9 31.3
    YOLOv6s 95.2 38.7 45.2 31.6
    YOLOv7 96.1 71.3 105.2 25.1
    YOLOv5s−FSW 95.8 13.1 14.3 36.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-27
  • 修回日期:  2024-05-06
  • 网络出版日期:  2024-06-13

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