# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 5 Conclusions  
In this paper, a study was conducted on CL, which has recently attracted increased interest owing to various problems, including the continuously increasing amounts of data, data storage, and copyright issues. CL is gradually increasing its application area in domains such as object detection and semantic segmentation in classification problems.  
In this study, KD was applied to the object detection field, and CenterNet, which does not use an anchor box, was changed to a CL model. In addition, by distilling the predictions of the t-model, we proposed a model that can minimize catastrophic forgetting and guarantee some new learning accuracy if there are sufficient learning data each time the next order gradually increases according to the CL scenario.  
Because the general object detection deep learning model uses an anchor box, manual work is required for predefined data, but as the increase in the size of the objects cannot be defined in advance, the anchor-free model was used to solve the problem of the anchor box among various hyperparameters.  
We proposed GT' to apply the idea of knowledge distillation to the anchor-free model for class IL. In our work, we used a disjoint data structure method that prohibits the reuse of data. The proposed model in our work avoids increasing parameters, and it does not store feature maps in previous learning. The t-model in the proposed model can label new training data. We call it t-pred, and it uses the labeling data of the t-model in our next learning. T-pred combines with GT to become GT', which minimizes confusion in model learning, although only new data are included in the new learning material.  
For the method proposed in this study, the loss value of the distillation layer should be specified, and the t-model prediction value and IoU threshold used by the t-model are also important hyperparameters; therefore, they should be adjusted according to the amount of new data added and the validity of the t-model's prediction in the image. These problems can also be efficiently applied to the real environment through the proposal of models that enable hyperparameter automation.  
Figure 5: The GT’ problems and solutions: (**a**) Labeling of GT’; (**b**) the result of IoU \(>\) 0.8 labeling.  
In fact, although many researchers are interested in CL, there have been limited studies applying CL to this point. One potential reason for this is that there are many areas where CL should be applied, but it is difficult to collect data for application. We have previously studied how deep learning is applied to the PCB field, and we have already collected a great deal of data on this topic. We believe that detecting PCB parts based on the proposed CL model will help cope with the increasing number of PCB parts in the future.  
Conceptualization, S.G. and J.L.; Data curation, S.G.; Formal analysis, D.C.; Funding acquisition, J.L.; Investigation, S.G.; Methodology, S.G., D.C. and J.L.; Project administration, J.L.; Resources, S.G.; Software, S.G.; Supervision, J.L.; Validation, S.G.; Visualization, S.G.; Writing--original draft, S.G.; Writing--review and editing, S.G., D.C. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R11A3074667).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the request of the company that provided the data.  
The authors have no conflict of interest relevant to this study to disclose.