Research On Knowledge Distillation Methods For Object Detection Networks

Object detection is a core task in computer vision,which is widely used in autonomous driving,security surveillance and other fields.How to reduce model size and improve model efficiency while ensuring model performance is a hot issue for research in object detection.In recent years,knowledge distillation methods,which transfer knowledge from complex models(teacher networks)to simple models(student networks)as a way to improve the performance of simple models,have been widely used to solve this problem.Existing knowledge distillation methods for object detection networks mainly focus on the distillation of the backbone network feature maps,while the determination of the feature distillation regions mostly relies on accurate labeling information,which is often difficult and expensive to obtain.In addition,there have been many studies on the distillation of feature maps,but the attention to the important performance metric of localization in detection networks is still insufficient.This study compared the differences between the proposal generation of the teacher network and the student network in object detection models.We found that the confidence information of the teacher network is crucial for reducing the dependence of the distillation process on annotation information.Based on this,we proposed a confidencebased distillation method that uses the confidence information of the teacher network to guide the generation of "soft labels" during the distillation process and to select the feature distillation region.Additionally,we introduced confidence-guided feature distillation to make the student network have feature representations closer to those of the teacher network.The proposed algorithm not only improves the performance of the model but also does not rely on accurate annotation information,making it suitable for distillation learning with unlabeled data.In addition,based on the introduction of unlabeled data for confidence distillation,this study also analyzed two types of critical data in the knowledge distillation process for semi-supervised learning: samples that are difficult for the teacher network to learn and samples that are difficult for the student network to imitate.To address the imbalance between difficult and easy samples,we further proposed a semi-supervised adaptive distillation algorithm.Adequate experimental results demonstrate that the proposed distillation method effectively improves the detection performance of lightweight detectors.When combined with semi-supervised adaptive distillation learning,the inference accuracy of the model is further improved,achieving good detection results.Therefore,this method has significant theoretical and practical value and provides a new solution for unlabeled data object detection tasks.