According to the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision encompasses several key capabilities: image classification, object detection, facial detection, and optical character recognition (OCR). When the task requires counting the number of distinct objects (in this case, animals) in an image, object detection is the correct type of vision model.
Object detection not only classifies what is present in an image but also identifies where each object appears by drawing bounding boxes around them. Each detected object is individually labeled, enabling the system to count or track them accurately. In contrast, image classification would only tell you the overall category (e.g.,
"This is an image of animals") without counting how many animals are present.
Facial detection focuses solely on identifying human faces, while OCR extracts text from images - neither applies here.
Therefore, the AI-900 official learning modules confirm that object detection is the appropriate solution for identifying and counting multiple entities within an image.