Publication Date: 2024/01/23
Abstract: Pallet racking systems are shelves that are specifically intended to hold palletised items, and they are essential for the safe and effective handling of products in warehouses. These shelves are susceptible to damage from a variety of sources, including as wear and tear and collisions, which might jeopardise their structural integrity and put workers and stored items at risk. It's critical to identify faulty pallet racking quickly to avoid mishaps, product loss, and interruptions to business operations. Pallet racking system upkeep and routine inspections, however, can be expensive and prone to human mistakes. This research study suggests Pallet-Net, a unique deep learning technique that employs an attention-based convolutional neural network (CNN) to automatically detect faulty pallet racking, as a solution to this problem. The suggested technique uses attention processes to concentrate on the pallet racking image's damaged areas, making it easier to locate and identify damage. Pallet-Net precisely categorises the racking as either damaged or undamaged by learning the discriminative properties of these zones. The suggested approach, when compared to previous studies, provides great robustness and accuracy in locating and recognising damaged areas in pallet racking photos. Moreover, the proposed method obtains a 97.64% total accuracy rate, with 98% precision, 98% recall, and 98% F1 score. Recent deep learning models like Vision Transformer (ViT) and Compact Convolutional Transformer (CCT) are also analysed and compared to the suggested architecture.
Keywords: Pallet Racking Systems; Logistics; Material Handling; Structural Integrity; Deep Learning; Attention Mechanisms; Convolutional Neural Networks; Image Classification; Spatial Transformer Network; Vision Transformer; Compact Convolutional Transformer.
DOI: https://doi.org/10.5281/zenodo.10555424
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN241.pdf
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