Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections

被引：52

作者：

Hussain, Muhammad ^{[1
]}

Al-Aqrabi, Hussain ^{[1
]}

Munawar, Muhammad ^{[2
]}

Hill, Richard ^{[1
]}

Alsboui, Tariq ^{[1
]}

机构：

[1] Univ Huddersfield, Sch Comp & Engn, Dept Comp Sci, Huddersfield HD1 3DH, W Yorkshire, England

[2] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 45550, Pakistan

来源：

SENSORS | 2022年 / 22卷 / 18期

关键词：

defect detection; deployment; rack damage; smart manufacturing; warehouse automation;

D O I：

10.3390/s22186927

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%.

引用

页数：13

共 30 条

[1] Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks [J].