基于改进YOLOv5的带式输送机大块煤检测

秦宇龙; 程继明; 任一个; 王晓晴; 赵青; 安翠娟

doi:10.13272/j.issn.1671-251x.2023080096

基于改进YOLOv5的带式输送机大块煤检测

doi: 10.13272/j.issn.1671-251x.2023080096

1.
北京科技大学自动化学院，北京　100083
2.
煤炭科学技术研究院有限公司，北京　100013

基金项目: 国家自然科学基金资助项目（62273035）。

详细信息

作者简介:
秦宇龙（2000—），男，湖北荆门人，硕士研究生，研究方向为3D目标检测，E-mail：18810592978@163.com

中图分类号: TD528.1
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出版历程
- 收稿日期: 2023-08-27
- 修回日期: 2024-02-21
- 网络出版日期: 2024-03-05

Large coal detection for belt conveyors based on improved YOLOv5

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
CCTEG China Coal Research Institute, Beijing 100013, China

摘要

摘要: 过大的煤块在带式输送机上运输时易造成煤流不畅、堵塞及堆煤，然而大块煤和普通煤块在外形和颜色上的差异较小，且煤块间存在遮挡和堆叠的情况，现有煤块检测方法对大块煤与普通煤块的区分不够精确，容易出现漏检或误检。针对上述问题，提出了一种改进YOLOv5模型，用于带式输送机大块煤检测。利用并行空洞卷积模块替换YOLOv5骨干网络中的部分普通卷积模块，扩大感受野，提升多尺度特征学习能力，从而更好地区分大块煤与普通煤块；在颈部网络中加入联合注意力模块，更好地融合上下文信息，提高对大块煤的定位能力。利用训练好的改进YOLOv5模型对摄像仪采集的实时输煤视频进行检测，根据大块煤的数量信息实时联动PLC示警。实验结果表明：相比于原始YOLOv5模型，改进YOLOv5模型在召回率和平均精度上分别提高了3.4%，2.0%；PLC可根据改进YOLOv5模型检测出的大块煤数量操作相应的指示灯和蜂鸣器进行示警；将改进YOLOv5模型应用于煤矿井下实际输煤视频中，对大块煤的检测精确率达97.0%，有效避免了漏检和误检现象。
- 带式输送机 /
- 大块煤检测 /
- YOLOv5 /
- 空洞卷积 /
- 注意力机制 /
- PLC联动示警 /
- 感受野
Abstract: Oversized coal blocks can easily cause poor coal flow, blockage, and coal stacking when transported on a belt conveyor. However, the differences in appearance and color between large coal blocks and ordinary coal blocks are small, and there are obstructions and stacking between coal blocks. Existing coal block detection methods are not precise enough to distinguish between large coal blocks and ordinary coal blocks, which can easily lead to missed or false detections. In order to solve the above problems, a modified YOLOv5 model is proposed for detecting large coal blocks in belt conveyors. The model uses parallel dilated convolution modules to replace some ordinary convolution modules in the YOLOv5 backbone network. It expands the receptive field, improves multi-scale feature learning capability, and better distinguishes large coal blocks from ordinary coal blocks. The joint attention module is added to the neck network to better integrate contextual information and improve the positioning capability for large coal blocks. The model uses the trained improved YOLOv5 model to detect real-time coal transportation videos captured by the camera, and links PLC alarms in real-time based on the quantity information of large coal blocks. The experimental results show that compared to the original YOLOv5 model, the improved YOLOv5 model has improved recall and average precision by 3.4% and 2.0%, respectively. PLC can operate corresponding indicator lights and buzzers to alert based on the quantity of large coal blocks detected by the improved YOLOv5 model. The improved YOLOv5 model is applied to actual coal transportation videos in coal mines, with a detection precision of 97.0% for large coal blocks, effectively avoiding missed and false detections.
- belt conveyor /
- large coal detection /
- YOLOv5 /
- dilated convolution /
- attention mechanism /
- PLC linkage warning /
- receptive field

HTML全文

图 1 煤矿带式输送机大块煤检测原理

Figure 1. Detection principle of large coal blocks in coal mine belt conveyor

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图 2 改进YOLOv5模型

Figure 2. Improved YOLOv5 model

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图 3 并行空洞卷积模块结构

Figure 3. Structure of parallel dilated convolution module

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图 4 联合注意力模块结构

Figure 4. Structure of joint attention module

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图 5 PLC联动示警流程

Figure 5. Flow of PLC linkage alarm

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图 6 PLC联动示警结果

Figure 6. Results of PLC linkage alarm

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图 7 实际场景检测结果

Figure 7. Detection results of actual scene

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表 1 消融实验结果

Table 1. Results of ablation experiments

并行空洞卷积模块	联合注意力模块	精确率/%	召回率/%	平均精度/%
×	×	95.4	90.1	94.3
√	×	95.5	92.7	95.2
×	√	95.9	91.7	94.6
√	√	95.6	93.5	96.3

下载: 导出CSV

表 2 不同模型性能对比结果

Table 2. Performance comparison results of different models

模型	召回率/%	平均精度/%	帧速率/（帧·s⁻¹）
Faster R−CNN^[21]	89.4	92.8	28.6
YOLOv5	90.1	94.3	58.4
YOLOv5+SCConv^[8]	90.5	94.2	60.2
YOLOv5+GnBlock^[10]	91.8	94.8	37.3
YOLOv5+DCBS3+DCTR	93.5	96.3	39.2

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表 3 不同模型检测精度对比结果

Table 3. Precision comparison results of different models

模型	精确率/%	平均精度/%
YOLOv5	88.3	90.9
YOLOv5+SCConv	93.9	94.3
YOLOv5+GnBlock	93.0	95.5
YOLOv5+DCBS3+DCTR	97.0	96.8

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