Design of Industrial Identification and Positioning System for Intelligent Workshop

ZHANG Hong-bin; LÜ Yong-ning; LI Chang-xing

doi:10.12263/newIT.2025.03.004

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Design of Industrial Identification and Positioning System for Intelligent Workshop

RESEARCH PAPER | 更新时间：2025-12-01

- Design of Industrial Identification and Positioning System for Intelligent Workshop
- New Generation of Information Technology Vol. 8, Issue 3, Pages: 19-23(2025)
- 作者机构：
  
  杭州腾岳电子有限公司技术部，浙江杭州 310013
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2025.03.004
  CLC： TP391
- Revised：2025-05-04，
  
  Accepted：06 May 2025，
  
  Published：15 March 2025
- 稿件说明：
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张宏斌, 吕永宁, 李昌兴. 智能车间工业识别与定位系统设计[J]. 新一代信息技术, 2025, 8(3): 19-23

ZHANG Hong-bin, LÜ Yong-ning, LI Chang-xing. Design of Industrial Identification and Positioning System for Intelligent Workshop[J]. New Generation of Information Technology, 2025, 8(3): 19-23
张宏斌, 吕永宁, 李昌兴. 智能车间工业识别与定位系统设计[J]. 新一代信息技术, 2025, 8(3): 19-23 DOI： 10.12263/newIT.2025.03.004.

ZHANG Hong-bin, LÜ Yong-ning, LI Chang-xing. Design of Industrial Identification and Positioning System for Intelligent Workshop[J]. New Generation of Information Technology, 2025, 8(3): 19-23 DOI： 10.12263/newIT.2025.03.004.

摘要

随着智能制造的快速发展，热处理车间工件的精确识别与定位成为提升生产效率和质量控制的关键环节。然而，传统单一技术方案在复杂工业环境下面临识别精度不足、定位误差大、抗干扰能力弱等问题。针对这些挑战，本文提出了一种融合机器视觉、无线射频识别（Radio Frequency IDentification，RFID）与超宽带（Ultra Wide Band，UWB）技术的多源异构系统架构，通过改进目标检测算法（You Only Look Once，YOLO）v5模型，引入注意力机制（Efficient Channel Attention，ECA）优化目标检测性能，设计多源数据融合算法实现厘米级定位精度。实验结果表明，该系统在复杂热处理环境下工件识别准确率达到98.7%，平均定位误差控制在±3.5 cm以内，相比单一技术方案提升了35%的识别效率和42%的定位精度。本研究为热处理车间智能化升级提供了可靠的技术支持，对推动工业4.0在特殊制造环境中的实践应用具有重要价值。

Abstract

With the rapid development of intelligent manufacturing

precise identification and positioning of workpieces in heat treatment workshops have become key links in improving production efficiency and quality control. However

traditional single technology solutions face problems such as insufficient recognition accuracy

large positioning errors

and weak anti-interference capabilities in complex industrial environments. In response to these challenges

this article proposes a multi-source heterogeneous system architecture that integrates machine vision

radio frequency identification (RFID)

and ultra wide band (UWB). By improving the YOLOv5 model and introducing efficient channel attention (ECA) attention mechanism to optimize object detection performance

a multi-source data fusion algorithm is designed to achieve centimeter level positioning accuracy. The experimental results show that the system achieves a workpiece recognition accuracy of 98.7% in complex heat treatment environments

with an average positioning error controlled within ±3.5 cm. Compared with a single technical solution

it improves recognition efficiency by 35% and positioning accuracy by 42%. This study provides reliable technical support for the intelligent upgrade of heat treatment workshops

which is of great value in promoting the practical application of Industry 4.0 in special manufacturing environments.

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