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.