Image Recognition in Retail: How Far Can It Go Now?

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Technological advancements of the last year are astonishing! This is especially true when it comes to the abilities of artificial intelligence. What started as narrow-purpose software transformed into a sophisticated system that can provide an answer to almost anything you ask of it.

The retail industry is reaping the benefits of AI, just like many others. There are a variety of solutions retailers can use to make their businesses more efficient, but one solution stands out.

Image recognition technology is a type of AI that can analyze images thanks to deep learning and provide users with actionable information. It also utilizes machine learning for continuous performance growth.

The number of retailers using real-time image recognition is already considerable and continues to grow with each year. According to the statistics, the market size of retail image recognition was 1.4 billion USD in 2020 and, based on forecasts, will grow to 46.7 billion USD by the end of 2024.

So, let’s dive into the specifics of what image recognition for retail can do.

Use Cases of Image Recognition Technology in Retail

Now that we have covered all the benefits that image recognition for retail brings, let’s focus on how it’s put to use. We compiled several use cases you might be interested in.

Search by Image

Image recognition helps customers find what they want faster. It works both in-store and online, with the difference that the system will check the store’s inventory instead. Search by image comes in three different flavors:

• Visual search, which allows users to upload an image of an item, and the system will find matching products in the store’s stock;
• Visually similar search, in cases when the store doesn’t have the exact copy but has similar items;
• Personalized product recommendations that analyze which items user likes and presents them with a variety of complementary items

Product Tagging

For a better customer experience, a lot of businesses in the retail sector use tags for easier item searches. However, these tags need to be added manually, and that can take employees a lot of time — but not with image recognition! The system can automatically add tags to products by analyzing its image. This way, businesses save time for their employees, and customers have an easier time searching for what they want.

Planogram Compliance

A planogram is a scheme of optimal product placement for increased sales. Item placement can be another time-consuming task for employees, but image recognition is here to help once again! It scans the store shelves, detects products and classifies them based on retail execution and stock-keeping unit (SKU) options. Then, it ensures planogram compliance by matching the current placement to the desired one notifying employees if there are mismatches.

Product Audits

Traditionally, retail audits are done by humans. This method has two issues: it is time-intensive and convoluted, making employee workloads heavier and leaving a lot of room for human error.

Unlike humans, AI is programmed to always be attentive and not lose track of anything. This is why, by using image recognition to conduct audits, retail stores can get more consistent and valuable data.

Self-Checkout Systems

Customers tend to prefer self-checkout options compared to the traditional cashier experience. There are two main ways businesses implement self-checkout.

The first one is placing machines where customers can scan products themselves. The second one is more interesting since it relies on image recognition-powered cameras to detect which customer took what item and automatically deduct the payment from their linked account.

AR Systems

Visualization of items in real life helps customers better understand whether they want it or not. This results in better customer satisfaction and positively reflects on sales.

Image recognition for retail solutions solves this problem. By combining augmented reality with item images, the system can model how a product will look on a customer or in their interior.

Smart Mirrors

Out of several ways businesses can use this technology, one stands out the most — smart mirrors. Imagine a mirror that can recommend clothes based on what the customer is wearing at the moment. Or skincare products by just looking at their face and determining skin type.

This is what a smart mirror is. Needless to say, customers love how they provide personalized recommendations and look like something out of science fiction.

Obstacles of Image Recognition in Retail and How to Overcome Them

There are two challenges a business might encounter: technological limitations and hardware limitations. While hardware limitations are easy to overcome by providing sufficient machines, technological limitations are in a different ballpark. Let’s discuss what to expect and solutions to the potential hurdles in execution.

Partially Hidden Objects

Retail image recognition is great at object detection. However, to properly detect an item, it needs to have a clear view. This might be a problem in real life since shops tend to have customers, and they can obstruct the view of items, causing technical issues. This problem is hard to overcome, but it’s not impossible. Using enhanced computer vision AI models is the answer.

Camera Angles

Picture this: your store has a shelf with items and a camera that sees the shelf from an angle. Items are visible, but they appear distorted due to camera placement. This might cause the image recognition system to fail to detect items on the shelf. But why?

To recognize an item, an image recognition system must have the item’s visual data. Usually, retail solutions with image recognition are trained on product images that cover all sides of the product. However, in the real world, cameras often will see items from unconventional angles. To fix this problem, you need to diversify the image recognition dataset with pictures of the product from different angles.

Shape Change

Some store items may change shape over time. A good example is folded/unfolded clothes. This might confuse the image recognition system if it wasn’t trained to recognize the changed shape of an item. By diversifying your dataset with images of the item’s changed shape, you can mitigate this problem.

Lighting Conditions

Light quality tends to change throughout the day. While in most cases this isn’t a problem, some may experience troubles with item detection due to overexposure to light and the use of insufficient quality cameras. Luckily, image normalization can fix this problem.

A Mess in the Background

Anyone can find a needle on the table. Not many can find a needle in a haystack. The same principle applies to image recognition. The system can detect an item with an empty background. In the real world, however, the likelihood of an empty background is questionable. Image segmentation solves this problem.

These challenges might look menacing, but when it comes to AI models, there’s a solution that almost always works. By providing an image recognition system with a diverse dataset that covers weird angles of items, items on cluttered backgrounds, different lighting levels on items, etc., you can solve all of these problems. The diverse dataset of product images leads to better outcomes, allowing retailers not to worry about these challenges.

What benefits can these image recognition use cases bring to retailers, and what are the insides of image recognition projects? Find the answers in our original article.