Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.
In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.
Let's evaluate the other options:
* Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.
* Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.
* Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.
Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.
# Final answer: Object detection