基于Stair−YOLOv7−tiny的煤矿井下输送带异物检测

梅晓虎; 吕小强; 雷萌

doi:10.13272/j.issn.1671-251x.18172

基于Stair−YOLOv7−tiny的煤矿井下输送带异物检测

doi: 10.13272/j.issn.1671-251x.18172

梅晓虎^1,,
吕小强²,
雷萌^3, ,

1.
国家能源集团宁夏煤业有限责任公司枣泉煤矿，宁夏银川　750000
2.
天地（常州）自动化股份有限公司，江苏常州　213015
3.
中国矿业大学信息与控制工程学院，江苏徐州　221116

基金项目: 国家自然科学基金青年科学基金项目（51904197)；天地（常州）自动化股份有限公司科研项目（2022FY0009）。

详细信息

作者简介:
梅晓虎（1986—），男，宁夏银川人，高级工程师，硕士，现从事煤矿信息化与智能化建设方面的工作，E-mail：15054039@ceic.com

通讯作者:
雷萌（1987—），女，安徽砀山人，副教授，博士，研究方向为计算机视觉与智能检测，E-mail:lmsiee@cumt.edu.cn。

中图分类号: TD528/634
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-09
- 修回日期: 2024-08-20
- 网络出版日期: 2024-09-06

Foreign object detection of coal mine underground conveyor belt based on Stair-YOLOv7-tiny

1.
Zaoquan Coal Mine, CHN Energy Ningxia Coal Industry Co., Ltd., Yinchuan 750000, China
2.
Tiandi (Changzhou) Automation Co., Ltd., Changzhou 213015, China
3.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要: 针对现有煤矿井下输送带异物检测方法应对复杂场景适应性差、无法满足实时性和轻量化要求、处理尺寸差异较大异物时表现不佳的问题，基于轻量化YOLOv7−tiny模型进行改进，提出了一种Stair−YOLOv7−tiny模型，并将其用于煤矿井下输送带异物检测。该模型在高效层聚合网络（ELAN）模块中添加特征拼接单元，形成阶梯ELAN（Stair−ELAN）模块，将不同层级的低维特征与高维特征进行融合，加强了特征层级间的直接联系，提升了信息捕获能力，增强了模型对不同尺度目标和复杂场景的适应性；针对检测头引入阶梯特征融合（Stair−fusion），形成阶梯检测头（Stair−head）模块，通过逐层融合不同分辨率的检测头特征，增强了中低分辨率检测头的特征表达能力，实现了特征信息的互补。实验结果表明：Stair−YOLOv7−tiny模型在输送带异物开源数据集CUMT−BelT上的检测效果优于CBAM−YOLOv5，YOLOv7−tiny及其轻量化模型，准确率、平均精度均值、召回率和精确率分别达98.5%，81.0%，82.2%和88.4%，检测速度为192.3帧/s；在某矿井下输送带监控视频分析中，Stair−YOLOv7−tiny模型未出现漏检或误检，实现了输送带异物的准确检测。
- 输送带异物检测 /
- YOLOv7−tiny /
- 多尺度目标检测 /
- Stair−fusion /
- 高效层聚合网络 /
- 检测头
Abstract: The existing methods for detecting foreign objects in underground coal mine conveyor belts have poor adaptability to complex scenarios, cannot meet real-time and lightweight requirements, and perform poorly when dealing with foreign objects with large size differences. In order to solve the above problems, a Stair-YOLOv7-tiny model is proposed based on the lightweight YOLOv7-tiny model for improvement, and applied to the detection of foreign objects in coal mine underground conveyor belts. This model adds feature concatenation units to the efficient layer aggregation network (ELAN) module to form a Stair-ELAN module. The model fuses low dimensional features from different levels with high-dimensional features, strengthens the direct connection between feature levels, enhances information capture capabilities, and strengthens the model's adaptability to objects of different scales and complex scenes. The introduction of Stair-head feature fusion (Stair-fusion) for detection heads forms a Stair-head module. The model enhances the feature expression capability of medium and low resolution detection heads by fusing detection head features of different resolutions layer by layer, achieving complementary feature information. The experimental results show that the Stair-YOLOv7 tiny model has better detection performance than CBAM-YOLOv5, YOLOv7 tiny, and its lightweight model on the open-source dataset CUMT BelT for conveyor belt foreign objects. The accuracy, average precision, recall, and precision are 98.5%, 81.0%, 82.2%, and 88.4%, respectively, and the detection speed is 192.3 frames per second. In the video analysis of conveyor belt monitoring in a certain mine, the Stair-YOLOv7-tiny model does not have any missed or false detection, achieving accurate detection of foreign objects in the conveyor belt.
- conveyor belt foreign object detection /
- YOLOv7-tiny /
- multi scale object detection /
- stair feature fusion /
- efficient layer aggregation network /
- detection head

HTML全文

图 1 Stair−YOLOv7−tiny模型结构

Figure 1. Structure of Stair-YOLOv7-tiny model

下载: 全尺寸图片幻灯片

图 2 Stair−ELAN模块结构

Figure 2. Structure of stair-efficient layer aggregation networks（ELAN） modular

下载: 全尺寸图片幻灯片

图 3 异物尺寸分布

Figure 3. Distribution of foreign object size

下载: 全尺寸图片幻灯片

图 4 损失函数值变化曲线

Figure 4. Loss function value change curve

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图 5 不同模型的输送带异物检测结果

Figure 5. Conveyor belt foreign object detection results of different models

下载: 全尺寸图片幻灯片

表 1 不同模型性能对比结果

Table 1. Performance comparison results of different models

模型	精确率/%	召回率/%	平均精度均值/%	准确率/%	浮点运算数/10⁹	单帧推理时间/ms	检测速度/（帧·s⁻¹）
CBAM−YOLOv5	87.8	80.3	78.2	94.7	113.2	16.2	61.7
YOLOv7−tiny	85.3	80.1	76.5	94.2	13.0	4.8	208.3
YOLOv7−tiny−Ghost	87.5	79.5	78.0	95.4	10.4	5.9	169.5
YOLOv7−tiny−MobileNetv2	85.1	79.0	76.3	93.1	15.2	14.3	69.9
YOLOv7−tiny−ShuffleNetv2	86.1	74.4	75.2	91.6	9.1	7.0	142.9
Stair−YOLOv7−tiny	88.4	82.2	81.0	98.5	19.3	5.2	192.3

下载: 导出CSV

表 2 消融实验对比结果

Table 2. Comparison results of ablation experiment

Stair−ELAN	Stair−head	精确率/%	召回率/%	平均精度均值/%	准确率/%	单帧推理时间/ms	检测速度/（帧·s⁻¹）
×	×	85.3	80.1	76.5	94.2	4.8	208.3
√	×	86.5	81.4	77.8	95.3	4.9	204.1
×	√	87.1	82.0	79.3	97.1	5.1	196.1
√	√	88.4	82.2	81.0	98.5	5.2	192.3

下载: 导出CSV

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