基于改进YOLOv8n的采掘工作面小目标检测方法

薛小勇; 何新宇; 姚超修; 蒋泽; 潘红光

doi:10.13272/j.issn.1671-251x.2024060013

基于改进YOLOv8n的采掘工作面小目标检测方法

1.
陕西陕煤澄合矿业有限公司，陕西渭南　715200
2.
西安科技大学电气与控制工程学院，陕西西安　710054
3.
中煤科工集团常州研究院有限公司，江苏常州　213015

基金项目: 陕西省秦创原“科学家+工程师”队伍建设项目（2022KXJ-38）。

详细信息

作者简介:
薛小勇（1976—），男，陕西铜川人，工程师，主要从事煤矿灾害治理工作，E-mail：2534187585@qq.com

通讯作者:
潘红光（1983—），男，山东临沂人，副教授，博士，研究方向为模型预测控制、人工智能及其应用。E-mail: hongguangpan@163.com。

中图分类号: TD67
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出版历程
- 收稿日期: 2024-06-02
- 修回日期: 2024-08-15
- 网络出版日期: 2024-08-01
- 刊出日期: 2024-08-30

Small object detection method for mining face based on improved YOLOv8n

1.
Chenghe Mining Co., Ltd., Shaanxi Coal and Chemical Industry Group Co., Ltd., Weinan 715200, China
2.
College of Electric and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
3.
CCTEG Changzhou Research Institute, Changzhou 213015, China

摘要

摘要: 为有效检测和识别煤矿井下采掘工作面人员是否佩戴安全防护装置，针对井下光照条件差、安全防护装备目标尺寸小且颜色与背景相似等情况，提出了一种基于改进YOLOv8n的采掘工作面小目标检测方法。在YOLOv8n骨干网络C2f模块中融合动态蛇形卷积（DSConv），构建C2f−DSConv模块，以提高模型提取多尺度特征的能力；在Neck层引入极化自注意力（PSA）机制，以减少信息损失，提高特征表达能力；在Head层增设1个专门针对小目标的检测头，形成4检测头结构，以扩大模型检测范围。实验结果表明，改进YOLOv8n模型对井下人员及其所佩戴安全帽、矿灯、口罩、自救器检测的平均精度分别为98.3%，95.8%，89.9%，87.2%，90.8%，平均精度均值为92.4%，优于Faster R−CNN，YOLOv5s，YOLOv7，YOLOv8n模型，且检测速度达208帧/s，满足煤矿井下目标检测精度和实时性要求。
- 采掘工作面 /
- 小目标检测 /
- YOLOv8n /
- 安全防护装备检测 /
- 多尺度目标识别
Abstract: In order to effectively detect and recognize whether the personnel on the mining face in coal mines are wearing safety protection devices, a small object detection method based on improved YOLOv8n is proposed. It is applied in situations such as poor underground lighting conditions, small object sizes of safety protection device, and similar colors to the background. The method integrates Dynamic Snake Convolution (DSConv) into the C2f module of YOLOv8n backbone network to construct a C2f DSConv module, in order to enhance the model's capability to extract multi-scale features. The method introduces polarized self-attention (PSA) mechanism in the Neck layer to reduce information loss and improve feature expression capability. The method adds one detection head specifically designed for small objects at the Head layer, forming a four detection head structure to expand the detection range of the model. The experimental results show that the improved YOLOv8n model has an average precision of 98.3%, 95.8%, 89.9%, 87.2%, and 90.8% for detecting underground personnel and their safety helmets, mining lights, masks, and self rescue devices, respectively. The average precision is 92.4%, which is better than Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8n models. The detection speed reaches 208 frames per second, meeting the requirements of object detection precision and real-time performance in coal mines.
- mining face /
- small object detection /
- YOLOv8n /
- safety protection equipment testing /
- multi scale object recognition

HTML全文

图 1 改进YOLOv8n模型结构

Figure 1. Improved YOLOv8n model structure

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图 2 C2f−DSConv结构

Figure 2. C2f-DSConv structure

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图 3 PSA机制的并行布局模块

Figure 3. Parallel layout module of polarized self-attention （PSA）

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图 4 4检测头结构

Figure 4. Four detection heads structure

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图 5 5类标签标注结果

Figure 5. Five categories of label annotation

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图 6 不同目标检测模型检测结果对比

Figure 6. Comparison of detection results of different object detection models

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表 1 实验平台配置

Table 1 Experimental platform configuration

配置	参数
操作系统	Windows10
CPU	Intel Core i7−12700K
GPU	NVIDIA GeForce RTX 3060
内存	32 GiB
GPU加速工具	CUDA11.1

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表 2 消融实验结果

Table 2 Ablation experiment results %

YOLOv8n	DSConv	检测头	PSA	精确率	召回率	mAP50
√	×	×	×	86.9	85.9	89.1
√	√	×	×	87.4	89.3	89.7
√	√	√	×	88.0	90.1	91.1
√	√	√	√	89.3	91.3	92.4

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表 3 不同目标检测模型对5种类别目标检测的AP对比

Table 3 Average precision (AP) comparison of detecting five categories by use of different object detection models %

类别	Faster− RCNN	YOLOv5s	YOLOv7	YOLOv8n	改进YOLOv8n
人员	84.2	92.9	94.2	97.7	98.3
安全帽	80.7	90.1	91.7	93.7	95.8
矿灯	68.7	76.6	76.3	79.8	89.9
口罩	74.3	82.9	83.9	86.2	87.2
自救器	73.3	81.7	81.4	85.1	90.8
mAP50	79.2	85.6	86.3	89.1	92.4

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表 4 不同目标检测模型的检测性能对比

Table 4 Comparison of detection performance of different object detection models

模型	参数量/MiB	GFLOPs	mAP/%	检测速度/（帧·s⁻¹）
Faster R−CNN	53.0	887.5	79.2	7
YOLOv5s	7.2	16.0	85.6	59
YOLOv7	36.9	104.7	86.3	142
YOLOv8n	3.0	8.1	89.1	457
改进YOLOv8n	3.4	13.3	92.4	208

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