Object detection for retail shelf compliance
A large consumer goods manufacturer uses an object detection model that identifies various products from shelves to measure compliance of their product placement on retail shelves. It uses numerous algorithms to calculate compliance percentage with a model planogram to identify various products, count their stock and make sure they are arranged in right order.
Increased sales by enhancing the customer convenience.
Reduced the labor cost for manually checking compliance.
The requirement was to identify various products, count the stock and make sure they are arranged in right order. An object detection model was used to identify numerous products on shelves and various algorithms to calculate compliance percentage with a model planogram. It detected the empty spaces, thereby avoiding the out-of-stock situations. It used object detection deep learning model to find the misplaced items and the empty spaces on racks. It increased the customer convenience by providing well organized products on racks.