Faster RCNN

Faster R-CNN is a two-stage object detection method that first proposes regions of interest and then classifies and refines their bounding boxes. Current research focuses on improving its accuracy and speed across diverse applications, including weapon detection, railway map analysis, and animal identification in camera trap images, often employing modifications to the network architecture or incorporating techniques like knowledge distillation and feature alignment. These advancements demonstrate Faster R-CNN's versatility and its significant impact on various fields requiring robust and accurate object detection, particularly in scenarios with challenging conditions like class imbalance or scale variation.