What is 2-stage detector

The 2-stage detector is a widely used object detection method in computer vision. It employs a two-phase approach where the first stage generates candidate regions and the second stage classifies and regresses these regions.

One of the most prominent examples of a 2-stage detector is Faster R-CNN, which is utilized in various applications such as autonomous driving, surveillance, and facial recognition. The workflow includes a Region Proposal Network (RPN) that creates potential bounding boxes in the first stage and classifies those boxes in the second stage.

These detectors are known for their high accuracy and ability to handle complex scenes, making them suitable for tasks like real-time detection of pedestrians and vehicles in self-driving cars.

However, they have a higher computational complexity compared to single-stage detectors like YOLO, making them slower in inference time. Future trends include optimizing these models for speed and integrating new architectures like Transformers.

When implementing a 2-stage detector, it's crucial to consider resource requirements and real-time performance, ensuring the right model and parameters for your specific application.