Object detection in computer vision refers to the task of identifying and locating objects within an image or video. The goal is to not only classify the objects present but also to determine their precise location by drawing bounding boxes around them. Object detection combines techniques from image classification, which identifies what an object is, and localization, which indicates where it is within the image. Examples include detecting people, cars, or animals in images. Modern object detection algorithms, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), have become popular due to their accuracy and speed. These models work by processing the entire image at once, allowing them to detect multiple objects in one pass. Applications of object detection include facial recognition in security systems, tracking moving objects in autonomous vehicles, and identifying defective items on assembly lines in manufacturing. Object detection is one of the most important tasks in computer vision and has wide-ranging applications across various industries.
What is object detection in computer vision?

- Getting Started with Zilliz Cloud
- Optimizing Your RAG Applications: Strategies and Methods
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Exploring Vector Database Use Cases
- AI & Machine Learning
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How will reasoning models evolve in the next decade?
Reasoning models are likely to become more sophisticated and tailored to specific tasks over the next decade. As artific
What are the challenges of detecting and tracking objects in videos?
Detecting and tracking objects in videos poses several challenges that can complicate the effectiveness of computer visi
How are feedback loops implemented in video search platforms?
Feedback loops in video search platforms are systems that enable the platform to learn from user interactions and improv