We live in a world that’s constantly innovating on how to cater for the speed with which things move – literally, as I came across an article about how Amazon fulfils its orders in its fulfilment centers using Computer Vision. In this blog, let us look at what is involved in Computer Vision Testing and the challenges involved.
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I come from a school of skepticism when it comes to testing. It’s about not taking things for granted. It’s about applying heuristics based on context, and questioning if the conclusions arrived are correct. At the same time, I am not against latest technologies like deep neural networks. Their abilities are fascinating, and as you might see in that article, their accuracy of identifying an item in a tote (a container used for a bag of items in a fulfilment center) is remarkable – which is 99%.
This advancement of using an item’s package and dimension details – to use cameras to take pictures and then match them against an existing library of scanned items – eliminates the need for barcodes in the packages. This is useful towards introducing robots to pick the items down the line, as robots cannot efficiently and effectively process an item as humans. If there is no barcode, robots need not look for the barcode and verify if it is the correct item.
While the above method sounds interesting, it has its challenges. As the article mentions, if two items are similar and have only minor differences like a tiny blue or a green dot on the package to indicate the color of the item inside the package, the machine learning algorithm might not be able to process it correctly. So, probability of being correct was introduced for the machine learning system to self-assess itself on the accuracy of the detection.
As a tester, I would have more things to worry about. What if two items are of same or very similar dimensions but contain totally different items? What if the camera is not able to catch the parameters accurately as expected? How would I validate the contents of the machine learning database to make sure that what we are comparing against are correct?
It’s an interesting world to live in. As the distribution and processing gets more and more automated for the sake of speed and efficiency, there are going to be more interesting challenges, and more needs for several new ways of validation and verification. Stay alert, and stay tuned!
If you need my expertise for computer vision testing for your organisation, feel free to contact me.
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