基于巡检机器人的吊装作业场景DPIM算法

doi:10.16265/j.cnki.issn1003-3033.2024.07.0235

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (7): 146-152.doi: 10.16265/j.cnki.issn1003-3033.2024.07.0235

基于巡检机器人的吊装作业场景DPIM算法

林世康¹^,²(), 侯庆文¹, 关淯尹^**^,³(), 王文财³, 李嘉禄³, 陈先中¹^,²

¹ 北京科技大学自动化学院,北京 100083
² 北京科技大学顺德创新学院,广东佛山 528399
³ 北京建筑材料科学研究总院有限公司,北京 100041

收稿日期:2024-01-12 修回日期:2024-04-13 出版日期:2024-09-20
通信作者:
^** 关淯尹（1991—），女，宁夏盐池人，硕士，工程师（人工智能），主要从事智能制造与机器视觉方面的工作。E-mail：guanyuyin@bbma.com.cn。
作者简介:
林世康 (2000—),男,福建福州人,硕士研究生,研究方向为人工智能和机器视觉。E-mail: 2219101802@qq.com。
侯庆文副教授；
陈先中教授
基金资助:
国家重点研发计划项目(2023YFB4706900); 广东佛山市科技创新项目(BK22BE022)

DPIM algorithm for hoisting operation scene based on inspection robot

LIN Shikang¹^,²(), HOU Qingwen¹, GUAN Yuyin^**^,³(), WANG Wencai³, LI Jialu³, CHEN Xianzhong¹^,²

¹ School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
² Shunde Innovation School, University of Science and Technology Beijing, Foshan Guangdong 528399, China
³ Beijing Building Materials Academy of Science Research, Beijing 100041, China

Received:2024-01-12 Revised:2024-04-13 Published:2024-09-20

摘要/Abstract

摘要：

为提高吊车安全作业的高精度检测预警,增强企业安全管理能力,围绕工业安全事件分析与监测预警无人化的需求,定制吊装场景地面和空中飞行相结合的巡检机器人,智能化吊装过程安全规程监控、弹窗图像记录和安全告警。首先,制作包含3 120张图片的吊装数据集Cranes-Dataset (CRN-Dataset),提出一种动态视角智能监测 (DPIM) 算法,以增强人-车-物多尺度目标的快速检测能力;然后,依据多帧图像的角点检测和带噪声基于密度的聚类方法,以及吊车与作业工人空间距离的安全属性,制定安全规则触发告警的流程,实时记录违规操作图像并弹窗预警。结果表明:经过实际部署和验证,DPIM算法相较于其他传统算法,吊装作业目标识别能力有明显提高,且适用嵌入式边缘智能分析节点的实时计算与数据传输,完成危险区域人员拒止的现场部署。

关键词: 巡检机器人, 吊装场景, 动态视角智能监测(DPIM)算法, 人员拒止, 边缘智能分析

Abstract:

In order to improve the high-precision detection and early warning of crane safety operation and enhance the safety management ability of enterprises, focusing on the needs of unmanned industrial safety incident analysis and monitoring and early warning, an inspection robot that combines ground and air flight in hoisting scene was customized to intelligentize hoisting safety monitoring, pop-up image recording and safety alarm.A lifting dataset Cranes-Dataset (CRN-Dataset) containing 3 120 images was made, and DPIM algorithm was proposed to enhance the rapid detection ability of multi-scale objects.Based on corner detection and density-based spatial clustering of applications with noise and considering the safety attributes of the space distance between cranes and workers, the process of triggering alarms based on safety rules was developed to record real-time illegal operation image and popup alarm.The results show that, after actual deployment and verification, the DPIM algorithm significantly improves target identification ability compared with other traditional algorithms, and it is suitable for real-time calculation and data transmission of embedded edge intelligent analysis nodes to complete field deployment.

Key words: inspection robot, hoisting scene, dynamic perspective intelligent monitoring (DPIM) algorithm, personnel denial, edge intelligent analysis

中图分类号:

X948

林世康, 侯庆文, 关淯尹, 王文财, 李嘉禄, 陈先中. 基于巡检机器人的吊装作业场景DPIM算法[J]. 中国安全科学学报, 2024, 34(7): 146-152.

LIN Shikang, HOU Qingwen, GUAN Yuyin, WANG Wencai, LI Jialu, CHEN Xianzhong. DPIM algorithm for hoisting operation scene based on inspection robot[J]. China Safety Science Journal, 2024, 34(7): 146-152.

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CRN-Dataset	图像	吊车	工人
训练集	2 184	2 380	2 795
验证集	624	665	802
测试集	312	333	400
总和	3 120	3 378	3 997

模型	P	R	mAP@0.5	FPS/(帧·s^-1)
YOLOv5l	0.862	0.771	0.778	47.6
Faster R-CNN	0.704	0.417	0.655	35.5
SSD	0.440	0.275	0.357	52.9
YOLOv8s	0.838	0.696	0.701	41.2
DPIM	0.894	0.809	0.831	39.4

模型	P	R	mAP@0.5	FPS/(帧·s^-1)
YOLOv5l	0.862	0.771	0.778	47.6
YOLOv5l+C2f	0.870	0.777	0.791	50.3
YOLOv5l+CA	0.882	0.790	0.815	36.9
YOLOv5l+C2f+CA	0.892	0.807	0.820	39.8

基于巡检机器人的吊装作业场景DPIM算法

DPIM algorithm for hoisting operation scene based on inspection robot

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