Moving toward Smart Manufacturing with an Autonomous Pallet Racking Inspection System Based on MobileNetV2

• Published in: Journal of Manufacturing and Materials Processing Vol. 6; no. 4; p. 75
• Main Authors: Hussain, Muhammad, Chen, Tianhua, Hill, Richard
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Algorithms; Automation; Computer vision; Damage; Deep learning; defect detection; deployment; Distribution centers; Fork lift trucks; Human error; Industry 4.0; Infrastructure; Inspection; Manufacturing; Pallets; rack damage; smart manufacturing; warehouse automation
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/jmmp6040075

Pallet racking is a fundamental component within the manufacturing, storage, and distribution centers of companies around the World. It requires continuous inspection and maintenance to guarantee the protection of stock and the safety of personnel. At present, racking inspection is manually carried out by certified inspectors, leading to operational down-time, inspection costs and missed damage due to human error. As companies transition toward smart manufacturing, we present an autonomous racking inspection mechanism using a MobileNetV2-SSD architecture. We propose a solution that is affixed to the adjustable cage of a forklift truck, enabling adequate coverage of racking in the immediate vicinity. Our proposed approach leads to a classifier that is optimized for deployment onto edge devices, providing real-time alerts of damage to forklift drivers, with a mean average precision of 92.7%.