基于改进YOLOv4的综采工作面目标检测

王科平; 连凯海; 杨艺; 费树岷

doi:10.13272/j.issn.1671-251x.2022070080

基于改进YOLOv4的综采工作面目标检测

doi: 10.13272/j.issn.1671-251x.2022070080

王科平^{1, 2,},
连凯海^{1, 2, ,},
杨艺^{1, 2},
费树岷³

1.
河南理工大学电气工程与自动化学院, 河南焦作　454003
2.
河南理工大学河南煤矿装备智能检测与控制重点实验室, 河南焦作　454003
3.
东南大学自动化学院, 江苏南京　210096

基金项目: 河南省科技攻关计划项目（212102210390）。

详细信息

作者简介:
王科平（1976—），女，河北张家口人，副教授，博士，主要研究方向为图像清晰化处理、目标检测、深度学习，E-mail：wangkp@hpu.edu.cn

通讯作者:
连凯海（1997—），男，河南南阳人，硕士研究生，主要研究方向为目标检测、深度学习，E-mail：15139027117@163.com

中图分类号: TD67
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出版历程
- 收稿日期: 2022-07-29
- 修回日期: 2023-02-16
- 网络出版日期: 2022-09-23

Target detection of the fully mechanized working face based on improved YOLOv4

WANG Keping^{1, 2
,},
LIAN Kaihai^{1, 2
, ,},
YANG Yi^{1, 2},
FEI Shumin³

1.
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2.
Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Henan Polytechnic University, Jiaozuo 454003, China
3.
School of Automation, Southeast University, Nanjing 210096, China

摘要

摘要: 综采工作面关键设备及人员的准确检测是实现煤炭智能化开采信息感知的重要环节。传统目标检测算法通过人工提取特征实现目标检测，易受环境影响，不具有普适性。基于卷积神经网络的目标检测算法可以自适应地提取深层信息，但复杂环境下检测精度不高、网络参数多、计算量大。针对上述问题，提出了一种改进YOLOv4模型，并将其应用于综采工作面目标检测。为准确从综采工作面复杂环境中检测到目标，在CSPDarkNet53网络中融入残差自注意力模块，保证参数共享及高效局部信息聚合的同时增强全局信息获取能力，提升图像关键目标特征表达能力，进而提高目标检测精度；为适应综采工作面目标检测高效性需求，引入深度可分离卷积替代传统卷积，以减少模型参数量和计算量，有利于模型的工业部署，提高目标检测速度。实验结果表明，与YOLOv3、CenterNet及YOLOv4模型相比，改进YOLOv4模型平均精度均值最高，达92.59%，且在参数量、计算量、检测精度上具有更优的平衡，可在煤尘干扰、光照不均、目标运动等复杂环境下对目标准确检测。
- 综采工作面 /
- 目标检测 /
- YOLOv4 /
- 残差自注意力 /
- 深度可分离卷积
Abstract: The accurate detection of key equipment and personnel in the fully mechanized working face is an important link to realize the information perception of intelligent coal mining. The traditional target detection algorithm realizes the target detection by extracting the features manually. But it is easily affected by the environment and it is not universal. The target detection algorithm based on the convolutional neural network can extract deep information adaptively. But the detection precision is not high, the network parameters are too many, and the calculation is too large in complex environment. In order to the above problems, an improved YOLOv4 model is proposed and applied to the target detection of the fully mechanized working face. In order to accurately detect targets in the complex environment of a fully mechanized working face, a residual self-attention module is integrated into the CSPDarkNet53 network. The capability of acquiring global information is enhanced while parameter sharing and efficient local information aggregation are ensured. The capability of expressing the features of key targets in an image is improved, and the target detection precision is further improved. In order to meet the requirement of high efficiency of target detection in the fully mechanized working face, depthwise-separable convolution is introduced to replace traditional convolution. The model parameter quantity and calculation quantity are reduced. It is beneficial to the industrial deployment of the model. And it improves target detection speed. The experimental results show that compared with YOLOv3, CenterNet and YOLOv4 models, the average precision of the improved YOLOv4 model is the highest, up to 92.59%. It has better balance in parameter quantity, calculation quantity and detection precision. It can accurately detect the target in the complex environment such as coal dust interference, uneven lighting and motion blur.
- fully mechanized working face /
- target detection /
- YOLOv4 /
- residual self-attention /
- depthwise-separable convolution

HTML全文

图 1 改进YOLOv4模型结构

Figure 1. Improved YOLOv4 model structure

下载: 全尺寸图片幻灯片

图 2 RSA模块结构

Figure 2. Residual self-attention module architecture

下载: 全尺寸图片幻灯片

图 3 深度可分离卷积操作

Figure 3. Depthwise separable convolution operation

下载: 全尺寸图片幻灯片

图 4 不同模型检测结果

Figure 4. Detection results of different models

下载: 全尺寸图片幻灯片

表 1 不同模型在井下综采工作面数据集上的检测结果

Table 1. Test results of different models on data set of underground fully-mechanized mining face

类别	AP/%
类别	YOLOv3	CenterNet	YOLOv4	改进YOLOv4
护帮板	97.28	89.92	97.97	98.50
采煤机	94.86	94.84	97.51	97.90
滚筒	94.46	95.80	96.23	96.87
大煤块	90.57	90.90	93.44	94.65
行人	84.14	87.41	89.67	91.17
线槽	81.41	82.91	87.21	89.93
刮板输送机	65.64	72.75	73.14	79.14
mAP/%	86.91	87.79	90.74	92.59

下载: 导出CSV

表 2 不同模型检测性能对比

Table 2. Comparison of detection performance of different models

模型	输入大小	参数量/10⁶	模型大小/MB	FLOPs/10⁹	mAP/%
YOLOv3	416×416	61.56	246.5	32.78	86.91
CenterNet	416×416	32.67	130.9	23.14	87.79
YOLOv4	416×416	63.97	256.3	29.90	90.74
改进YOLOv4	416×416	33.11	133.2	19.48	92.59

下载: 导出CSV

表 3 消融实验结果

Table 3. Ablation experiment results

模型	mAP/%	参数量/10⁶
YOLOv4	90.74	63.97
YOLOv4+深度可分离卷积	90.25	35.71
YOLOv4+深度可分离卷积+RSA	92.59	33.11

下载: 导出CSV

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