基于 MobileNetV2 的边缘平台自动托盘货架检查:迈向智能制造

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-01-27 DOI:10.1007/s10723-023-09738-y
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引用次数: 0

摘要

摘要 托盘式货架是全球企业使用的生产、存储和配送网络中的一个关键要素。为了确保员工的安全和存货的保护,需要进行持续的检查和维护。目前,经过认证的检查员需要手动检查货架,这就造成了操作延误、服务费用和人为失误造成的遗漏损坏。随着企业向智能制造转型,我们介绍了一种利用集成框架 MobileNetV2--只看一次(YOLOv5)的自动货架评估方法。所提出的方法可检查自动托盘跟踪系统,并根据托盘装载过程中的边缘平台检测多种损坏。它将 YOLOv5 与块开发机制 (BDM) 结合使用,后者可检测有缺陷的托盘货架。我们提出了一种装置,该装置可安装在叉车的活动笼上,并为邻近的货架提供足够的覆盖范围。此外,我们还将任何损坏分为重大损坏和轻微损坏,以便楼层主管根据具体情况决定是否需要立即更换。这将为货架行业提供一种持续监控货架的方法,而不是每年或每季度进行一次货架检查,从而创造一个更安全的工作环境。我们建议的方法可以生成一个专门安装在边缘设备上的分类器,为叉车操作员提供便利。
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Automated Pallet Racking Examination in Edge Platform Based on MobileNetV2: Towards Smart Manufacturing

Abstract

Pallet racking is a critical element of the production, storage, and distribution networks businesses worldwide use. Ongoing inspections and maintenance are required to ensure the workforce's safety and the stock's protection. Currently, certified inspectors manually examine racks, which causes operational delays, service charges, and missing damages because of human error. As businesses move beyond smart manufacturing, we describe an automated racking assessment method utilizing an integrated framework, MobileNetV2-you only look once (YOLOv5). The proposed method examines the automated pallet tracking system and detects multiple damages based on edge platforms during pallet racking. It employs YOLOv5 in conjunction with the Block Development Mechanism (BDM), which detects defective pallet racks. We propose a device that attaches to the moveable cage of the forklift truck and provides adequate coverage for the neighboring racks. Also, we classify any damage as significant or minor so that floor supervisors can decide whether a replacement is necessary immediately in each circumstance. Instead of conducting annual or quarterly racking inspections, this would give the racking industry a way to continuously monitor the racking, creating a more secure workplace environment. Our suggested method generates a classifier tailored for installation onto edge devices, providing forklift operators.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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