Object detection with object counting

Object detection is a computer vision technique used to identify and locate multiple objects within an image or video. It not only recognizes what the objects are but also determines where they are by drawing bounding boxes around them. This technology is widely used in various fields such as autonomous vehicles for detecting pedestrians and other cars, surveillance systems for monitoring activities, retail environments for tracking inventory and customer behavior, healthcare for analyzing medical images, and robotics for helping machines interact with their surroundings.

We present an object detection model designed to identify and classify various products at a checkout counter. Traditional checkout systems rely on barcode scanning, which poses challenges for items like bread, fruits, or vegetables that typically lack barcodes. Our solution addresses this by using a camera-based object detection system that automatically recognizes these items without manual input. This not only streamlines the checkout process but also enhances user convenience and efficiency by eliminating the need to manually search for or select products.

Our system runs entirely on an embedded device, with all processing performed locally. Despite its compact size, the model is efficient and capable of accurately detecting and classifying a wide range of product types. This makes the solution both practical and scalable for real-time checkout applications without relying on external servers or cloud processing.

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