融合人体关键点的实验室PPE规范穿戴检测方法

doi:10.16182/j.issn1004731x.joss.25-0415

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (5): 1365-1382.doi: 10.16182/j.issn1004731x.joss.25-0415

• • 上一篇

融合人体关键点的实验室PPE规范穿戴检测方法

彭莉峻¹, 苏庭琪², 刘沛津², 何林³, 周协武², 张闽心²

^1.西安建筑科技大学工程综合实训中心，陕西西安 710055
^2.西安建筑科技大学机电工程学院，陕西西安 710055
^3.西安建筑科技大学理学院，陕西西安 710055

收稿日期:2025-05-12 修回日期:2025-07-22 出版日期:2026-05-21 发布日期:2026-05-29
通讯作者: 刘沛津
第一作者简介:彭莉峻(1986-)，女，高级实验师，硕士，研究方向为计算机视觉应用、电力电子技术。
基金资助:
陕西省重点研发计划项目(2022GY-134);陕西省教育厅专项科研项目(21JK0732);西安建筑科技大学自然科学专项项目(ZR19058);陕西省教育厅科研计划项目(24JC048)

Detection Method for Laboratory PPE Compliance Wearing Based on Human Key Points

Peng Lijun¹, Su Tingqi², Liu Peijin², He Lin³, Zhou Xiewu², Zhang Minxin²

^1.Engineering Comprehensive Training Center, Xi'an University of Architecture and Technology, Xi'an 710055, China
^2.School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
^3.School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China

Received:2025-05-12 Revised:2025-07-22 Online:2026-05-21 Published:2026-05-29
Contact: Liu Peijin

摘要/Abstract

摘要：

针对实验室复杂环境中，实验人员穿戴安全防护装备多尺度、多类目标检测时存在漏检率高、穿戴规范性判断不准等问题，提出一种融合多尺度特征与人体关键点的实验室人员PPE (personal protective equipment)规范穿戴检测方法(multi-scale multi-target joint key point detection method, MSMT-JKDM)。引入多尺度自适应下采样模块与级联组注意力变换器，增强实验室检测场景下PPE的特征表达能力，有效缓解小目标易被忽略、特征混淆等问题；采用选择性边界感知多尺度重校准金字塔网络，通过多尺度融合、边界重校准及注意力引导的方式，提升跨尺度特征的表达能力。设计自适应无锚检测头，以提升多尺度目标的定位精度和检测效率。在此基础上，通过将防护装备和人体部位两个对象分支获得的相关坐标信息进行联合推断，准确判断实验人员PPE穿戴的规范性。仿真实验结果表明：MSMT-JKDM的mAP50、mAP50~95以及规范穿戴性指标SWA较基线模型提升3.7%、6.2%和5%；在数据集GDUT-HWD和SHWD上mAP50分别达到82.8%和87.9%，优于现有主流检测方法，进一步验证了模型的有效性。

关键词: 实验室人员, PPE穿戴规范, MSMT-JKDM (multi-scale multi-target joint key point detection method), 多尺度多目标检测, 关键点检测

Abstract:

To address the problems of high missed detection rate and inaccurate judgment of wearing compliance when multi-scale and multi-category targets of laboratory personnel's safety protective equipment are detected in a complex laboratory environment, this paper proposesa laboratory personnel's standard personal protective equipment (PPE) wearing detection method (multi-scale multi-target joint key point detection method, MSMT-JKDM) that integrates multi-scale features and human key points. The multi-scale adaptive down sampling (MSA-Down) module and the cascaded group attention transformer (CGA Former) are introduced to enhance the feature representation ability of PPE (especially small targets such as goggles and gloves) in laboratory detection scenarios, effectively alleviating the problems of small targets being easily ignored and feature confusion. To solve the problem that directly fusing multi-scale features of different types of PPEs in complex environments may lead to feature redundancy, spatial misalignment, and inconsistent object representation, the selective boundary-aware multi-scale recalibration feature pyramid network (SBMSRFPN) is adopted to improve the representation ability of cross-scale features through multi-scale fusion, boundary recalibration, and attention guidance. An adaptive anchor-free detection head (AAF-Head) is designed to improve the positioning accuracy and detection efficiency of multi-scale targets. The relevant coordinate information obtained from the two object branches of protective equipment and human body parts is jointly inferred to accurately judge the standardization of PPE wearing of the experimenters. Experimental verification is carried out on this dataset. The results show that the mAP50, mAP50-95, and standard wearability index standardized wearing accuracy (SWA) are improved by 3.7%, 6.2%, and 5%, respectively compared with those of the baseline model. The mAP50 reaches 82.8% and 87.9% on the public datasets GDUT-HWD and SHWD, respectively, better than that of the existing mainstream detection methods, further verifying the effectiveness of the model.

Key words: laboratory personnel, PPE wearing regulations, MSMT-JKDM, multi-scale multi-target detection, key point detection

中图分类号:

TP391.9

彭莉峻,苏庭琪,刘沛津等 . 融合人体关键点的实验室PPE规范穿戴检测方法[J]. 系统仿真学报, 2026, 38(5): 1365-1382.

Peng Lijun,Su Tingqi,Liu Peijin,et al . Detection Method for Laboratory PPE Compliance Wearing Based on Human Key Points[J]. Journal of System Simulation, 2026, 38(5): 1365-1382.

图/表 19

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图10

表1

图11

表2

不同算法在 DCDLE数据集上的对比结果

模型	P/%	R/%	mAP50/%	$m A P$ 50~95/%	计算量×10⁹	参数量×10⁶	SWA/%
YOLOv5n	91.4	81.5	86.3	57.7	7.1	25.0	71.7
YOLOv6n	90.3	85.1	87.5	61.0	11.8	42.3	85.0
YOLOv8n	91.0	83.7	87.7	60.2	8.1	30.1	68.3
YOLOv9t	91.6	82.7	87.3	60.0	7.6	19.7	96.7
YOLOv10n	78.5	71.9	79.6	53.8	8.2	26.9	75.0
YOLO11n	88.0	84.1	86.7	58.0	6.3	25.8	93.3
YOLOv12n	88.7	81.5	85.6	57.9	5.8	25.1	75.0
Hyper-YOLO	90.3	84.3	88.2	59.5	9.5	36.2	70.0
RT-DETRl	71.0	75.1	78.3	54.2	100.6	284.6	95.0
RT-DERT-resnet50	78.6	80.3	85.0	60.3	125.7	419.5	86.7
Ours	94.0	85.1	89.9	61.6	10.6	28.7	98.3

表2

表3

表4

表5

表6

图12

图13

参考文献 23

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关键点	Class 1 (正确穿戴)	Class 2 (未正确穿戴)	验证逻辑
Left eye	glass	unglass	护目镜是否覆盖左眼
Right eye	glass	unglass	护目镜是否覆盖右眼
Left shoulder	coat	uncoat	实验服是否覆盖左肩
Right shoulder	coat	uncoat	实验服是否覆盖右肩
Left hip	coat	uncoat	实验服是否覆盖左髋
Right hip	coat	uncoat	实验服是否覆盖右髋
Left wrist	glove	unglove/mismatch	手套是否覆盖左手腕
Right wrist	glove	unglove/mismatch	手套是否覆盖右手腕

模型	类别	P/%	R/%	mAP50/%	mAP50~95/%	P_C-SWA/%
YOLO11n	person	98.9	98.6	99.1	79.6
	coat	99.3	99.5	99.5	87.4	100
	glass	94.2	96.8	97.5	62.4	94.7
	glove	81.6	81.0	87.2	55.3	85.9
	uncoat	78.2	70.9	76.3	32.7
	unglass	98.5	96.1	97.6	71.6
	unglove	65.2	45.9	49.5	17.1
Ours	person	98.3	98.3	99.3	82.5
	coat	99.6	98.5	99.2	88.4	100
	glass	96.7	94.9	96.7	65.2	95.4
	glove	92.0	82.8	89.5	60.6	88.0
	uncoat	89.4	71.7	83.6	37.0
	unglass	98.9	97.0	97.7	77.2
	unglove	83.3	52.4	63.0	25.7

实验	MSA-Down	CGAFormer	SBMSRFPN	AFF-Head	mAP50/%	mAP50~95/%	P_SWA/%
1					86.7	58.0	93.3
2	√				87.9	60.3	92.5
3		√			88.7	59.4	93.6
4			√		88.9	60.3	94.7
5				√	87.2	58.4	95.7
6	√	√			88.4	59.9	96.2
7	√		√		89.7	61.0	97.2
8	√	√	√		88.8	61.2	97.9
9	√	√	√	√	89.9	61.6	98.3

模型	P/%	R/%	mAP50/%	mAP50~95/%	参数量×10⁶	计算量×10⁹
YOLOv5n	86.2	72.7	80.3	48.4	25.0	7.1
YOLOv8n	88.8	72.9	80.7	48.8	26.8	6.8
YOLOv9t	86.5	71.1	79.6	47.6	17.3	6.4
YOLOv10n	82.5	68.0	75.9	45.3	26.9	8.2
YOLO11n	85.4	74.6	80.9	48.4	25.8	6.3
YOLOv12n	86.9	72.8	80.0	47.1	25.1	5.8
RT-DERT-l	75.5	66.4	72.8	42.0	284.5	100.6
Hyper-YOLO	88.4	74.9	82.1	48.2	36.2	9.5
Ours	89.7	74.1	82.8	49.0	28.7	10.6

模型	P/%	R/%	mAP50/%	mAP50~95/%	参数量×10⁶	计算量×10⁹
YOLOv5n	88.7	80.4	87.2	51.9	25.3	7.1
YOLOv8n	88.9	80.1	87.9	52.7	30.1	8.1
YOLOv9t	88.8	79.8	86.9	52.1	19.7	7.6
YOLOv10n	84.7	76.4	84.1	49.8	26.9	8.2
YOLO11n	88.8	79.7	87.7	52.5	25.8	6.3
YOLOv12n	88.5	78.7	86.7	51.4	25.1	5.8
RTDERT-l	82.0	72.1	80.0	45.6	254.5	100.6
Hyper-YOLO	88.3	80.1	85.9	51.1	36.2	9.5
Improvement of Faster RCNN^[20]	82.2	79.2	80.3	54.4
Improvement of YOLOv4^[21]	88.4	74.0	79.4	44.1
Improvement of YOLOv5^[22]	85.3	75.2	81.2	52.6
BiFEL-YOLOv5s^[23]	86.5	77.9	82.8	57.7
Ours	90.2	80.8	87.9	59.0	28.7	10.6

融合人体关键点的实验室PPE规范穿戴检测方法

Detection Method for Laboratory PPE Compliance Wearing Based on Human Key Points

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