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Confluence Of AI On The Edge And Computer Vision In The Wood Pallets Industry


Intelligence is moving to the edge. As the growth of data acquisition accelerates, migration of intelligent computing closer to the edge allowing more efficient use of data at reduced latency and infrastructure cost. Computer vision is a field of artificial intelligence that trains machines to interpret and understand the visual world.

One of the goals of computer vision is for machines to see and process images in the same way humans do. Computer vision through machine learning uses a method called supervised learning where a large, annotated image set is used to construct a computational model. While model training can be intense and time-consuming, once trained, this model can quickly and effectively perform a variety of tasks.

Pallets transport goods throughout the world and are an integral part of the supply chain logistics industry. The European Federation of Wooden Pallet and Packaging Manufacturers (FEFPEB) reported that more than 3 billion wooden pallets are in circulation in the EU, while 2 billion wooden pallets are used each day in the U.S. The majority of these pallets are owned by large pooling solution companies which lease pallets from a shared pool, reducing complexity of pallets procurement, management and recovery for companies managing the supply chain of their products.

Some examples of image and video analysis include identifying, classifying, counting, and estimating pose of objects; compressing and encoding visual information for transmission or matching; estimating camera perspective; 2-D segmentation of foreground and background; estimating depth and performing 3-D segmentation; and inpainting or inferring visual data for occluded regions of an image.

With the recent technological advancement of edge AI, the ability to process and analyze data locally at the camera as opposed to streaming data to the cloud means that computer vision may be at the forefront of leveraging the wood pallet industry.

Three types of computer vision architecture exist: video or images that are sent to the cloud for computation; partial computing on the edge where only a few modalities are transferred to the cloud to search, sort and compute; and edge AI, which computes all image data on the edge. The latter requires training in a model to be installed on the edge and updated at frequent intervals. These architectures have unique advantages and disadvantages. With the growing body of computer vision research, it is possible to find a model that is well suited to each application.

Pooling solution companies want to monitor pallet movement throughout the supply chain to understand pallet losses and recovery, pallet damage and pallet cycle time. To achieve comprehensive monitoring, each pallet could be labeled with a unique identifier or a tracking device. If a pallet has a unique ID such as a barcode or a QR code, computer vision can be used to track the pallet as it flows through the supply chain. If a pallet is instrumented with a tracking device, it can be detected through computer vision for device replacement or maintenance as the pallet flows through the sortation process in a service center.

Pallets are made from a wide variety of wood, including beech, ash, poplar, pine and spruce. The type of wood the pallets are made from forms the unique composition of wood tissue contours that not only provide insight into how strong and durable a single pallet can be but can also provide a unique identifier for a pallet that can be used for tracking. Additionally, the footprint of nails that fasten the wooden boards with blocks form a topology that can provide additional information about the pallet life cycle, such as how long the pallet will last in the supply chain before it hits the repair belt.

Through well-trained computer vision models, the unique grain patterns of each pallet can be identified at birth, and this identity can be managed as the pallet flows through its life cycle. Changes to the wood patterns and structure can be tracked as the pallet is damaged and repaired as it cycles through the supply chain. Computer vision will not only allow tracking of pallets by just images, but it will also give insights into pallet strength and durability. This low-cost solution equips pallet companies to take action to filter unreliable pallets at birth or after repeated use. Furthermore, pallet logistics companies can gather insights into the number of damages and types of damages by customer and industry verticals to enhance the business model and improve the design to make the platforms more rugged.

With 95% of organizations and institutions reporting their continued use, wooden pallets continue to dominate the supply chain market. Wood is the only material that is 100% renewable, recyclable, reusable and rated for hygienic transport several classes of goods.

Computer vision can also play a crucial role in maintaining an accurate inventory count of pallets in any warehouse. From an image that contains one or more stacks of pallets, a well-trained neural network can produce a count of all pallets in less time and with more accuracy than a well-trained eye.

Beyond supply chain KPIs, computer vision can be applied to both worker safety and efficiency within the service centers where pallets are stored, inspected and repaired. Any repetitive task, such as nailing activity on a repair bench, can be fed into a model to identify worker accuracy, fatigue and many more actions from a live video stream. Additionally, human detection can be used to identify when people might be present in unauthorized or hazardous areas of a facility and if they are wearing appropriate PPE. This can help prevent workplace accidents, which is the highest priority in this business.

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