## 6 Concluding Remarks  
We discussed the difficulties of the object detection problem, and showed that the difficulties could be suppressed by a refinement stage which we proposed to be coupled with an already trained standard object detector during the inference. We proposed the refinement to be a single object detector that works over crops extracted from an input image according to the information produced by a standard object detector. To solve the problem when one extracted crop includes several objects, we proposed using mixture data where the image information was complemented with information about the object's class and center, which helped the network to refine the desired object.  
In the benchmark section, we demonstrated that BBRefinement is capable of increasing the mAP of 10 SOTA networks out of the 11 tested. The added overhead was 76 ms for BBRefinementB3 and for processing a single input image with multiple crops. Finally, we showed that our scheme, BBRefinement, is able to produce predictions that are in most cases more precise than the ground truth labels of a dataset.  
As the refinement process is partially independent of the detector, this approach opens a new direction of research. Original research which is focused on increasing accuracy by proposing new architectures, etc., can now be complemented with independent research on refinement networks. The final system, which can be deployed to real production in  
Figure 10: Ablation study. **Left**: influence of distortion center coordinates or incorrect class. **Right**: influence of crop size.  
various competitions (such as Kaggle or Signate), may consist of a combination of the best algorithms from both types of research. Future work remains to be completed to search for a BBRefinement backbone with switchable atrous convolutions or to further improve processing speed.  
We used the scheme of refinement in Signate's competition on object detection and tracking ([[https://signate.jp/competitions/256/leaderboard](https://signate.jp/competitions/256/leaderboard)]([https://signate.jp/competitions/256/leaderboard](https://signate.jp/competitions/256/leaderboard)), accessed on 20 December 201), where our solution based on Poly-YOLO [28] + BBRefinement ended as a runner-up--notwithstanding the fact that the international rivals used more powerful architectures such as RetinaNet or CenterTrack. In this competition, BBRefinement improved Poly-YOLO's mAP from 57.8 to 59.8 and proved its usefulness.  
The code of BBRefinement is available at [[https://gitlab.com/irafm-ai/bb-refinement](https://gitlab.com/irafm-ai/bb-refinement)]([https://gitlab.com/irafm-ai/bb-refinement](https://gitlab.com/irafm-ai/bb-refinement)), accessed on 2 March 2022.  
Conceptualization, P.H., M.V. and D.H.; data curation, D.H.; formal analysis, P.H., M.V. and D.H.; methodology, P.H. and D.H.; software, M.V. and P.H.; validation, P.H., M.V. and D.H.; visualization, M.V.; writing, P.H. and M.V. All authors have read and agreed to the published version of the manuscript.  
The work is supported by ERDF/ESF \"Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region\" (No. CZ.02.1.01/0.0/0.0/17_049/0008414).  
Not applicable.  
The source codes for implementing the presented approach are freely available at: [[https://gitlab.com/irafm-ai/bb-refinement](https://gitlab.com/irafm-ai/bb-refinement)]([https://gitlab.com/irafm-ai/bb-refinement](https://gitlab.com/irafm-ai/bb-refinement)), accessed on 2 March 2022.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
B. Bounding Box  
FPS. Frames per Second  
GT. Ground Truth  
IoU. Intersection over Union  
mAP. Mean Average Precision  
SOTA. State Of The Art