Abstract
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%.
| Original language | English |
|---|---|
| Article number | 75 |
| Number of pages | 17 |
| Journal | Journal of Manufacturing and Materials Processing |
| Volume | 6 |
| Issue number | 4 |
| Early online date | 8 Jul 2022 |
| DOIs | |
| Publication status | Published - 1 Aug 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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Dive into the research topics of 'Moving toward Smart Manufacturing with an Autonomous Pallet Racking Inspection System Based on MobileNetV2'. Together they form a unique fingerprint.Research output
- 30 Citations
- 1 Article
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Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections
Hussain, M., Al-Aqrabi, H., Munawar, M., Hill, R. & Alsboui, T., 13 Sept 2022, In: Sensors (Switzerland). 22, 18, 13 p., 6927.Research output: Contribution to journal › Article › peer-review
Open Access72 Link opens in a new tab Citations (Scopus)
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