基于改进YOLOv8的煤矿输送带异物检测

洪炎; 汪磊; 苏静明; 汪瀚涛; 李木石

doi:10.13272/j.issn.1671-251x.2024050006

基于改进YOLOv8的煤矿输送带异物检测

doi: 10.13272/j.issn.1671-251x.2024050006

安徽理工大学电气与信息工程学院，安徽淮南　232001

基金项目: 国家重点研发计划项目（2021YFD2000204）；国家自然科学基金项目（12304236，32301688，52174141）；安徽数字农业工程技术研究中心开放项目（AHSZNYGCZXKF021）；大学生创新创业基金项目（202210361053，202310361037）；安徽理工大学研究生创新基金项目（2024cx2067）。

详细信息

作者简介:
洪炎（1979—），男，重庆万州人，教授，博士，主要研究方向为图像处理与物联网，E-mail：hong5212724@163.com

通讯作者:
汪磊（1997—），男，安徽六安人，硕士研究生，主要研究方向为深度学习与嵌入式系统、目标检测，E-mail：wljy2023@163.com。

中图分类号: TD634.1
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出版历程
- 收稿日期: 2024-05-06
- 修回日期: 2024-05-25
- 网络出版日期: 2024-07-10

Foreign object detection of coal mine conveyor belt based on improved YOLOv8

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 现有基于深度学习的输送带异物检测模型较大，难以在边缘设备部署，且对不同尺寸异物和小目标异物存在错检、漏检情况。针对上述问题，提出一种基于改进YOLOv8的煤矿输送带异物检测方法。采用深度可分离卷积、压缩和激励(SE)网络将YOLOv8主干网络中C2f模块的Bottleneck重新构建为DSBlock，在保持模型轻量化的同时提升检测性能；为增强对不同尺寸目标物体信息的获取能力，引入高效通道注意力(ECA) 机制，并对ECA的输入层进行自适应平均池化和自适应最大池化操作，得到跨通道交互MECA模块，以增强模块的全局视觉信息，进一步提升异物识别精度；将YOLOv8的3个检测头修改为4个轻量化小目标检测头，以增强对小目标的敏感性，有效降低小目标异物的漏检率和错检率。实验结果表明：改进YOLOv8的精确度达91.69%，mAP@50达92.27%，较YOLOv8分别提升了3.09%和4.07%；改进YOLOv8的检测速度达73.92帧/s，可充分满足煤矿输送带异物实时检测的需求；改进YOLOv8的精确度、mAP@50、参数量、权重大小和每秒浮点运算数均优于SSD，Faster-RCNN，YOLOv5，YOLOv7−tiny等主流目标检测算法。
- 输送带异物检测 /
- YOLOv8 /
- SE网络 /
- 高效通道注意力机制 /
- 轻量化 /
- 小目标检测 /
- 自适应平均池化 /
- 自适应最大池化
Abstract: The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices. There are errors and omissions in detecting foreign objects of different sizes and small objects. In order to solve the above problems, a foreign object detection method for coal mine conveyor belts based on improved YOLOv8 is proposed. The depthwise separable convolution, squeeze-and-excitation (SE) networks are used to reconstruct the Bottleneck of the C2f module in the YOLOv8 backbone network as a DSBlock, which improves the detection performance while keeping the model lightweight. To enhance the capability to obtain information from objects of different sizes, an efficient channel attention (ECA) mechanism is introduced. The input layer of ECA is subjected to adaptive average pooling and adaptive maximum pooling operations to obtain a cross channel interactive MECA module, which enhances the global visual information of the module and further improves the precision of foreign object recognition. The method modifies the 3 detection heads of YOLOv8 to 4 lightweight small object detection heads to enhance sensitivity to small objects and effectively reduce the missed and false detection rates of small object foreign objects. The experimental results show that the improved YOLOv8 achieves a precision of 91.69%, mAP@50 reached 92.27%, an increase of 3.09% and 4.07% respectively compared to YOLOv8. The detection speed of improved YOLOv8 reaches 73.92 frames/s, which can fully meet the demand for real-time detection of foreign objects on conveyor belts in coal mines. The improved YOLOv8 outperforms mainstream object detection algorithms such as SSD, Faster-RCNN, YOLOv5, and YOLOv7-tiny in terms of precision, mAP@50, number of parameters, weight size, and number of floating point operations.
- foreign object detection on conveyor belts /
- YOLOv8 /
- SE network /
- efficient channel attention mechanism /
- lightweight /
- small object detection /
- adaptive average pooling /
- adaptive maximum pooling

HTML全文

图 1 YOLOv8网络结构

Figure 1. YOLOv8 network structure

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图 2 CED−YOLO网络结构

Figure 2. CED-YOLO network structure

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图 3 数据增强流程

Figure 3. Data enhancement process

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图 4 SC2f模块结构

Figure 4. SC2f module structure

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图 5 DSBlock模块结构

Figure 5. DSBlock module structure

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图 6 MECA模块结构

Figure 6. MECA module structure

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图 7 改进后检测层

Figure 7. Improved detection layer

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图 8 改进后轻量化检测头

Figure 8. Improved lightweight detection head

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图 9 数据集图像

Figure 9. Dataset images

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图 10 模型性能指标

Figure 10. Model performance indicators

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图 11 不同模型的识别结果

Figure 11. Recognition results of different models

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图 12 模型改进前后热力图对比

Figure 12. Comparison of heat maps before and after model improvement

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表 1 实验硬件配置

Table 1. Experimental hardware configuration

实验环境	配置
操作系统	Windows 10
CPU	Intel（R） Core（TM）i5−13490F CPU@2.50 GHz
GPU	NVIDIA GeForce GTX 4060（8 G）
深度学习框架	PyTorch 1.9.1+CUDA 11.1+CUDNN 8.0.5
编译器	Python 3.8.18
内存	32 GiB

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表 2 消融实验结果

Table 2. Ablation experiment results

序号	A	B	C	D	E	精确度/%	召回率/%	mAP@50/%	mAP@50∶95/%	参数量/ 10⁶个	权重大小/ MiB	每秒浮点运算数/10⁹	速度/ （帧·s⁻¹）
1	×	×	×	×	×	88.60	80.19	88.20	56.70	3.00	6.3	8.1	162.55
2	√	×	×	×	×	89.25	83.09	89.36	58.62	3.00	6.3	8.1	163.23
3	√	√	×	×	×	89.45	87.98	92.32	59.94	2.68	5.5	6.9	156.68
4	√	√	√	×	×	89.02	86.26	92.96	62.21	2.79	5.9	11.7	94.49
5	√	√	√	√	×	92.03	84.30	91.92	60.89	2.79	5.9	11.7	111.26
6	√	√	√	√	√	91.69	83.25	92.27	61.59	2.34	5.0	6.2	73.92

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表 3 主流算法对比结果

Table 3. Comparison results of mainstream algorithms

算法	精确度/%	mAP@50/%	参数量/ 10⁶个	权重大小/ MiB	每秒浮点运算数/10⁹
YOLOv3	87.54	89.06	12.12	24.4	18.9
YOLOv5	89.38	88.52	2.50	5.3	7.1
YOLOv7−tiny	84.40	89.70	6.01	12.3	13
YOLOv8	88.60	88.20	3.00	6.3	8.1
Faster−RCNN	66.13	55.09	136.73	108.0	401.7
SSD	74.05	65.20	23.87	91.09	274.0
文献[22]中算法	81.40	89.30	6.87	14.1	14.2
文献[24]中算法	90.60	89.60	1.92	4.1	4.7
CED−YOLO	91.69	92.27	2.34	5.0	6.2

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