This paper provides a comprehensive review of the recent advancements in knowledge distillation (KD)-based object detection (OD) models. It covers different KD strategies for improving object detection tasks, such as incremental OD, small object detection, and weakly supervised OD. The paper also explores advanced distillation techniques and highlights future research directions in the field.