Although object detection has achieved impressive progress and has a broad impact, it still encounters significant challenges in accurately detecting objects in open-world scenarios. The data in real-world open scenes often exhibit characteristics of limited annotations, such as very few annotated samples or even unknown classes with no annotations. Our studies mainly focus on applying detection to various open scenes and addressing the challenges of sparse samples and unknown classes. The comprehensive research aims to develop more powerful and efficient methods for object detection in the open world, making them more suitable for real-world applications.