Image Recognition

A picture of success: what exactly is image recognition?

Image recognition technology has come on leaps and bounds over the past few years. At one point, AI-based image recognition was limited to sci-fi blockbusters and spy flicks – we’ve all watched in awe as a team has had to pull off a long-winded heist to get into a vault – but now, its benefits can be found everywhere. Beyond security, image recognition applications are hard at work across the healthcare sector, assisting with microsurgical procedures and kinematics where an extra pair of automated eyes are needed.

The automobile industry and, unsurprisingly, the gaming sector are also using this clever tech to push the boundaries of innovation, where it makes its mark in streamlining production processes and harnessing facial recognition to help improve player immersion. 

So, while this all sounds fantastic – how does it work exactly?

An offshoot of artificial intelligence, image recognition is hard at work in nearly every aspect of our daily lives, with its machine-learning capabilities processing digital images or video to identify product attributes and features.

At Tactical Solutions, our own industry-leading image recognition solution, Recognise, sees the world differently. As a computer, its digital image processing understands that a picture is a picture – but looks deeper into the elements that form its makeup. An image recognition tool understands that pixels, with a wealth of numerical data, are the best representation of what we humans see with our eyes. It’s important to note that, without some deep learning, computers will struggle to differentiate images – this is because a computer ‘sees’ in numbers, not in concepts or any wider knowledge. Recognise, like most solutions that use machine recognition in retail, takes this numerical data and turns it into a tapestry of patterns and regularities, using these familiar aspects as a ‘representation’ of what it’s looking at. Pretty neat, isn’t it? 

However, image recognition should not be mixed up with object detection – another automatic human capability. Computers are fantastic – they wouldn’t have received such a rapid mass adoption if not – but they require some seriously smart technology to imitate our eyes and brains in understanding the world around us. For computers to process visual data in this way, image recognition technology is an effective solution. This is because, as the name suggests, it can process aspects of an external image and classify it into various product categories. 

How does this apply to retail businesses?

With so much potential, it’s understandable why we’d be so keen to get our mitts on this tech and put it to work in the retail industry. Already, there are plenty of competitive intelligence techniques used by field reps the world over that are helping to improve customer experience and streamline the packaging, processing, and promotion of consumer goods in multiple sectors. Even finer details, such as price detection and overall quality can be determined by visual content fed to these engines, helping to optimise overall shelf health for brands and customers alike. 

Amazingly, other factors such as advanced biometrics have been introduced to image recognition solutions, assisting brands and marketers in getting a complete picture of how their products are used and interacted with on the shop floor. We see this in action within a number of ways: 

Product audits: We know that the shelf is probably not the most interesting place when it comes to sourcing those key figures. In fact, the thought of manually auditing physical stores is enough to induce shudders around the entire Tactical Solutions team – which is why we’re glad that image recognition not only speeds up but smooths out the process. Our time is precious, which is why image recognition technology cuts out inaccurate audit data and messy retail executions that look nothing like the proposed layout – understandable, considering we’re all only human. But where our brains struggle with the regular alignment and standardisation of data, artificial intelligence thrives. This data, which can be interpreted on even the most granular level by computers, cannot only understand how much share of shelf each brand has but can also glean pricing trends and retail sales, too. There ain’t no audit like an intelligent audit. 

Planogram compliance: Ah, planograms. The backbone of any broader strategy when it comes to good, solid merchandising. We know that, on paper, a planogram is able to improve customer satisfaction thanks to a gorgeous-looking display bursting with consumer goods that never seem to run out, maximising sales and keeping everybody – from brand to buyer – happy. Under corporate instruction, retailers should know how to look after the general upkeep of a shelf according to the terms and conditions of a contract, but numerous factors, from short staffing to shipping problems, can cause issues with fulfilling that compliance. This is where technology can help brands bridge that vital gap – helping retailers and brands alike to stay at the top of their planogram game. 

Image recognition can use a single photo to determine whether the goods on the shelf match the reference planogram, creating a layered algorithm that shoots an alert about any discrepancies. We can’t be in hundreds of different places at once – but this technology allows brands to ensure that customer demand is being met, wherever their goods are sold. 

Empty shelf detection: Shopper frustration is a real issue. ‘Stockouts’ (otherwise called shelf shortages) is one of the leading causes of customer dissatisfaction, something that has been seen in spades as retailers struggle to fulfil increasing demand alongside supply issues. 

When shelves look barren and stores appear understocked, concerns start to grow on behalf of the consumer and brands – understandable, as this is the visual evidence of money literally disappearing from the industry’s regular income streams. It’s a looming problem that has been worsened by current geopolitical and worldwide impacts, but technology has been able to not only recoup but revolutionise how retailers can keep on top of their shelves for the good of their customers – and their monetary interests. When a vital SKU is missing from its place, image recognition applications are able to pick up the slack, helpfully notifying when staff needs to replace stock or put another order in. As our systems become even more intelligent, these machines can learn more complex algorithms to pre-empt a shortage, pulling up trend analysis and customer bases to ensure that business keeps booming. And who doesn’t love that?