Incremental Learning Strategy with Multi-dimensional Knowledge Distillation for One-stage Object Detection

In practical applications, object detectors often encounter unknown classes samples. As object detection datasets undergo continuous development, the need arises for object detectors to adeptly recognize an escalating array of new classes while retaining the original detection capability. This paper introduces a new class incremental object detection learning framework with multi-dimensional knowledge distillation, using Yolov5 as the foundational detection model. Specifically, we utilize local feature distillation, attention distillation and global feature distillation to preserve information in the original model's feature maps. Additionally, we employ response distillation to ensure the model remains responsive to previous classes. In comparison to the state-of-the-art methods, our approach achieves higher overall accuracy on PASCAL VOC 2007. Especially in the “19+1” incremental task, our approach gets the highest mAP@0.5 of 0.697 with a minimum mean AP decline rate of 4.45%. For the “4+3” and “6+1” tasks on KITTI, our approach preserves the model's responsiveness to the original task more effectively while achieving the highest overall accuracy.