YOLO-v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection

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The aim of this research is to develop an automated pallet inspection architecture with two key objectives: high performance with respect to defect classification and computational efficacy, i.e., lightweight footprint. As automated pallet racking via machine vision is a developing field, the procurement of racking datasets can be a difficult task. Therefore, the first contribution of this study was the proposal of several tailored augmentations that were generated based on modelling production floor conditions/variances within warehouses. Secondly, the variant selection algorithm was proposed, starting with extreme-end analysis and providing a protocol for selecting the optimal architecture with respect to accuracy and computational efficiency. The proposed YOLO-v5n architecture generated the highest MAP@0.5 of 96.8% compared to previous works in the racking domain, with a computational footprint in terms of the number of parameters at its lowest, i.e., 1.9 M compared to YOLO-v5x at 86.7 M.
Original languageEnglish
Article number120
Number of pages15
JournalBig Data and Cognitive Computing
Issue number2
Publication statusPublished - 14 Jun 2023

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