Two Stage Detector

Two-stage object detectors, characterized by separate proposal generation and classification stages, have been a mainstay in computer vision, but their computational cost has driven research towards efficiency improvements. Current efforts focus on optimizing these detectors for resource-constrained environments like on-device inference in remote sensing and video object detection, employing techniques such as feature pyramid simplification and leveraging temporal consistency across video frames to reduce computational burden while maintaining accuracy. These advancements are significant for real-time applications requiring rapid object detection in scenarios with limited processing power, such as autonomous vehicles and robotics.