Imagine warehouse staff having full visibility and connectivity for every step of a package ’ s journey through the facility .
Vision capabilities move beyond OCR Today , computer vision technologies in warehouse logistics are primarily optical character recognition ( OCR ) functionalities to improve sorting efficiency , accuracy and throughput . Taking static images from cameras mounted above conveyors , OCR can read and understand labels , addresses , barcodes and symbols , as well as handwritten and printed text . This information is used by people , sorting machines and robots to move items to the correct destination . Products such as Prime Vision ’ s Address Vision and Barcode Vision are augmented with AI to reconstruct and interpret damaged or obscured package information .
While impressive , nowadays computer vision is presenting even more opportunities . Cameras with higher resolutions , improved colour and 3D depth of field are allowing high-quality images and video to be collected . GPUs now offer the raw processing power required to handle this increased data , while deep learning techniques for text and object recognition mean that it can be more efficiently quantified . Excitingly for warehouse operators ,
these technologies are becoming more commercially accessible and less complex to implement .
Setting the scene for greater understanding With these kinds of capabilities , warehouse operators can now look beyond the conveyor . Instead of having one opportunity to read a label on a letter or parcel , what if warehouse staff could see and connect every event for a package travelling through the facility ?
This is known as ‘ scene understanding ’ and it allows errors to be reduced in even the most controlled sorting environments . By combining all information at a network level , problems can be spotted early and the whole process appraised , enabling proactive reasoning and a better approach to dealing with exceptions that otherwise may incur increased costs to the business .
How would this work in practice then ? Well , computer vision software would have access to all installed cameras in the facility , with an innate understanding of the relative position of each unit . This intelligence means less calibration and easier setup . With this extended field of view , the system is free to identify and help solve problems .
The system could help resolve parcel no reads for example . There is also the possibility to spot packages that are on top of each other or have become stuck together . It could also highlight non-machinable items by assessing dimensions , shape or the instability of an object , quickly separating items before they can cause issues for equipment or become damaged . For operations that handle a high variability of items like in the postal market , this streamlines how different packages are dealt with , saving time .
Beyond the parcel stream , computer vision can track roller cages to confirm they reach the right destination . The system can see whether doors are open or closed , pointing out where improvements to efficiency or safety could be made . Cameras on loading bays can monitor trucks moving in and out , allowing delivery trends to be analysed and further logistical insights gained .
Issue 72 PECM 103