Object Detection
Object detection is an advanced Computer Vision technique that allows locating and identifying multiple objects in an image or video. This technology surrounds each detected object with a bounding box, usually a rectangle, to precisely define its position.
Today, object detection plays a key role across various industries:
- Real-time video surveillance, for detecting and tracking people or vehicles.
- Autonomous vehicles, continuously analyzing their surroundings to avoid obstacles and detect traffic signs, pedestrians, or cyclists.
- Retail and e-commerce, enabling automated checkout without individually scanning products.
- Healthcare, for example, in automated analysis of medical images.
With this technology, algorithms don’t just detect the presence of an object—they can also classify it (e.g., person, animal, vehicle) and precisely locate it within the image (read our full guide).
The process begins with annotated datasets. Each training image is carefully labeled with bounding boxes and categories. The AI model learns to recognize shapes, textures, colors, and characteristic patterns. Once trained, it can apply this knowledge to new images or videos to identify and position objects (Innovatiana — Object Detection Guide).
Among the main approaches, two major families stand out:
- One-shot (single-stage) methods, such as YOLO (You Only Look Once): they divide the image into a grid and simultaneously predict bounding boxes and associated classes. This ultra-fast operation is ideal for real-time applications such as autonomous driving or continuous video monitoring (learn more about YOLO).
- Two-stage methods (e.g., R-CNN, Fast R-CNN, Faster R-CNN): they first generate region proposals and then analyze each area in more detail. While slower, they often deliver higher accuracy, making them suitable for use cases where precision is the top priority.
To dive deeper into these concepts, explore our article "Introduction to Object Detection in Computer Vision", which explains the fundamentals, algorithms, and real-world applications. And to understand how YOLO became one of the most popular and fastest object detection algorithms, read our article "What is YOLO in AI".
