Location of a damaged product → Yes
Multiple instances of the same product → Yes
Multiple types of damaged products → Yes
All three statements are Yes, because they correctly describe the capabilities of object detection, one of the major workloads in computer vision, as defined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module: “Describe features of computer vision workloads on Azure.”
Object detection is an advanced computer vision technique that allows AI systems not only to classify objects within an image but also to locate them by drawing bounding boxes around each detected object. This differentiates it from simple image classification, which only identifies what objects exist in an image without specifying their locations.
Identifying the location of a damaged product – YesAccording to Microsoft Learn, object detection can return the coordinates or bounding boxes for recognized objects. Therefore, if the model is trained to detect damaged products, it can pinpoint exactly where those defects appear within an image.
Identifying multiple instances of a damaged product – YesObject detection models can detect multiple objects of the same class in one image. For instance, if an image contains several damaged products, each will be identified and marked individually. This feature supports tasks such as automated quality inspection in manufacturing, where several defective units may appear simultaneously.
Identifying multiple types of damaged products – YesObject detection can also distinguish different classes of objects. When trained on multiple labels (e.g., cracked, scratched, or broken items), the model can detect and classify each type of damage in one image.
In Microsoft’s AI-900 framework, object detection is presented as a critical part of computer vision workloads capable of locating and classifying multiple objects and categories within visual content.
