3 posts
What is YOLO? YOLO (You Only Look Once) is a real-time object detection architecture that locates and classifies objects in an image in a single neural network pass. This guide: a clear definition, why YOLO matters, how it works, bounding boxes and the grid, YOLO versions, sector examples from Türkiye, KVKK, comparisons, and FAQs.
What is computer vision? Computer vision is the field of AI that lets a machine perceive and understand the content of images and videos like a human. This guide: a clear definition, how it differs from image processing, how computer vision works, CNNs and deep learning, object detection and image classification, enterprise use cases, and FAQs.
One of the most important design decisions in computer vision is choosing the correct task family for the problem. Image classification, object detection, and segmentation may appear to work on the same kind of visual data, but they differ significantly in output structure, error cost, annotation requirements, computational profile, and real-world usage. If the system only needs to answer “what is in the image?”, image classification may be sufficient. But when the question becomes “where is it?”, object detection becomes necessary. And when the need goes down to “which pixels belong to which object?”, segmentation is the more appropriate approach. This guide compares image classification, object detection, and segmentation from theoretical, methodological, and practical angles, showing where each task fits best, what kind of data and labels it needs, what failure patterns are common, and how they are used in real-world systems.