Authors :
Mujadded Al Rabbani Alif
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/nhcbdhhu
Scribd :
http://tinyurl.com/bdfraeuf
DOI :
https://doi.org/10.5281/zenodo.10555424
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.
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.
