Faster R CNN

Faster R-CNN is a two-stage object detection method aiming for accurate and efficient localization and classification of objects within images. Current research focuses on improving its robustness to various challenges, including handling low-quality data, addressing class imbalance, and enhancing generalization across different domains, often employing techniques like data augmentation, attention mechanisms, and knowledge distillation within the Faster R-CNN framework or by integrating it with other architectures like transformers. This work holds significance for numerous applications, from medical image analysis and industrial defect detection to autonomous navigation and wildlife monitoring, where reliable and efficient object detection is crucial.