An incremental object detection model based on knowledge distillation

This paper proposes an incremental object detection model framework based on knowledge distillation. The framework addresses the issue of catastrophic forgetting by optimizing the distillation loss function and expanding the labels of the new dataset based on the old object classes, which significantly enhances the recognition capability of the model for the old objects. The framework is implemented and evaluated on the two-stage object detection algorithm. Experimental results on the PASCAL VOC dataset demonstrate that the proposed model achieves competitive performance compared to state-of-the-art methods.