Product recognition software strengthens item-level oversight

 Product recognition in vending requires a photographic mechanism to match the image of a product in a spiral facing to a stored database of products to determine its stock keeping unit (SKU) code number and selling price. When integrated with nutritional information, product ingredient and nutrient content can also be accessed through VEII's MIND technology. While product recognition technology involves several important concepts and features designed to enhance effectiveness, the following five concepts are intended to provide an overview of the product recognition application; there are several additional characteristics and capabilities mentioned throughout this article.

Product Recognition – the object of product recognition is matching product images to stored photographs to confirm product identity. Matches lead to access to SKU, product pricing, and optional linkage to nutritional database information (when integrated with MIND software).

Digital Pricing – given product identification within the recognition system database, the price of an item can be accessed and posted as a digital price; the item's price is based on its stored price, not the item's location in the vending machine.

Diagnostic Tool – the camera component of product recognition can be used to check machine status, operational status, and overall appearance. An empty facing, hung-up product spiral, or an unrecognized product are conditions that may traditionally result in machine downtime can now be remotely diagnosed and resolved by remotely positioning the camera over the problem spiral and in real-time, remotely operating the spiral to visually see if the problem can be corrected.

Bijection – bijection is a process in which mathematical algorithms (formulas) are used to match a product image to stored file content. In the case of vending, the matching can be accomplished regardless of the orientation of the product package in the machine spiral. Bijection is designed to decipher physical attributes against database imagery (one-to-one recognition) and is similar to biometric measurement, except it does not include human characteristics or attributes.

MIND Software – a product recognition application can be optionally applied with the standalone application from VEII labeled Making Informed Nutritional Decisions (MIND). The MIND involves linking manufacturer supplied nutritional and ingredient information to vendible product offerings. When product recognition is integrated with nutrient and key ingredient data of the MIND, data files can be displayed on the touchscreen of the application.

How It Works

 In preparation for product recognition, a camera is used to capture a series of product images in varying orientations (e.g. right-side up, upside down, forward, backward) to create the highest probability of positive identification. This variance in angular views, combined with acknowledgement of font style, font size, and color, lead to minimal misevaluations or failed reads. The degree of detection effectiveness is an important concern whenever a bijection evaluation (one-to-one matching) is performed.

How Effective Is It?

 From a comparison perspective, snack products are considered easier to identify than bottled beverages that can rotate up to 360 degrees thereby necessitating more database images for identification matching. Although with snack items there may be some wrinkling or unreadable text, most packaging is of a bag or loose wrapping that places the item forward, backward, right-side up, or upside down thereby requiring relatively fewer database images to test for identity than the potential challenges of a bottled item.

Packaging that can be angled; uneven, wrinkled, or condensed might lead to false readings. While it may be impossible for the software algorithm to correctly identify every product in each spiral 100 percent of the time, the goal of product recognition software however is to achieve a correct recognition rate of at least 95 percent. When the packaging of two products may be so similar that differentiation may be based only on a small icon or graphic logo located somewhere off-center of the wrapper facing, distinguishing the correct product presents a complex recognition challenge.

For example, consider a Hershey Milk Chocolate candy bar and a Hershey Milk Chocolate with Almonds candy bar. The wrappers of the two bars are very similar in color, font, text, and shape. Discriminating between them through the lens of a digital camera, given the items are vertically slotted behind a spiral or other armature, illustrates the possible difficulties in attaining a perfect identification rate.