Continual Object Detection

Continual object detection focuses on enabling computer vision systems to learn to identify objects incrementally, adapting to new object classes and data distributions without forgetting previously acquired knowledge. Current research emphasizes mitigating "catastrophic forgetting" through techniques like knowledge distillation, replay methods, and parameter isolation, often applied to lightweight architectures like NanoDet for resource-constrained environments such as robotics. This field is crucial for advancing robust AI systems in dynamic real-world applications like autonomous driving and mobile robotics, where continuous learning and adaptation are essential for reliable performance.