Addressing Class Imbalance in Object Detection with YOLOv5 Framework

The author explores the challenges of foreground-foreground class imbalance in object detection, focusing on the YOLOv5 model. The study introduces a benchmarking framework and evaluates sampling, loss reweighing, and augmentation techniques to address this issue.