Computer vision gives systems the ability to see. When this technology is integrated with artificial intelligence (AI), machine learning, and deep learning systems, the results can be powerful for restaurants and retailers.
Computer vision systems include cameras that capture video or still images of the processes a business wants to improve, for example, moving cars through the line at a drive-thru or decreasing the wait for customers in a checkout line. Analyzing these images with AI provides insights into operations and shows businesses the next steps to take to keep things moving. Additionally, deep learning running on a neural network allows these systems to provide a more accurate analysis of the images. In other words, the system does a better job of emulating the kind of work that a human would do.
How Computer Vision Can Provide Value to Restaurants
Almost every aspect of a restaurant can be improved with real-time video analysis. Computer vision can enhance the quality of the customer experience, the quality of food the kitchen prepares, and the training offered to new employees.
Deep learning analysis of video can help restaurants with line volume management by understanding their busiest times, providing accurate wait time data, and valuable insights into line abandonment by assisting operators in knowing the “when” and “why” that drives it.
Order Inspection in Real Time
Getting a customer’s order wrong does competitors a favor. Monitoring food preparation in real time enables corrective intervention to ensure no order goes out the window, across the counter, or to a delivery driver without evidence that the order is accurate.
Safety and Hygiene Monitoring
While germ phobia of the early 2020s has decreased, sensible hygiene will never go out of style in the food service industry. AI monitoring can help enforce company policies and hand washing rules and ensure that mishandled food never gets into the customer’s order.
Food Waste Monitoring
AI analysis of returned plates can tell restaurant operators much about their food. Is a specific item being consistently thrown out? Are portions so large that customers can’t finish them? Is a particular chef often making food that gets discarded in larger volumes compared to other chefs? Computer vision can deliver quantifiable answers to these questions.
Many restaurants are now bringing on new employees, hiring whoever is willing to work rather than those with experience and skills. Real-time video analysis can help prompt inexperienced cooks on when to flip the steak on the grill or pull the basket out of the fryer. Creative developers can find ways to gamify restaurant training, using AI to make onboarding fun.
Retail Use Cases for Computer Vision
AI technology is having a transformative effect on retail. By strategically implementing AI, retailers can increase productivity, boost sales, and improve security. Creative solutions in these areas can reinvigorate retailers looking for an edge in competitive markets.
Self-Checkout and Cashierless Stores
The savvy use of computer vision and AI can add new dimensions to the customer experience, reduce loss, and save labor costs. With cameras that track the movement of customers and goods and sensors that detect when items are removed from a shelf, retailers can improve self-checkout security or move to a cashierless model.
By strategically positioning cameras throughout retail displays, AI can alert staff when items need replenishment. It can also compare customer traffic patterns to sale density to identify the best locations for in-store displays.
Layout Improvement with Heat Maps
Customer movement heat maps can identify flaws in store design that keep merchandise hidden from view. Through experimentation with AI, retailers can determine the best layout for their physical space and inventory.
Virtual Mirrors and Recommendations
Utilizing the power of augmented reality, retailers can allow customers to “try on” their entire inventory, promoting specials and tailoring recommendations for individuals.
Machine learning algorithms coupled with video cameras can be used to identify suspicious behaviors that indicate the possibility of fraud and theft. For example, by recognizing every item in a checkout area and connecting it with a transaction, computer vision can help identify when a cashier fails to scan every item.
Computer vision applications in retail and food service are still in their infancy, but their potential is undeniable. Solutions providers ready to educate themselves about this technology, form strategic partnerships, and expand their offerings to include computer vision solutions can create unique solutions that solve pain points for retailers and restaurateurs.
Explore how to use computer vision to bring your clients to the next level of automation and efficiency.