# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 6 Conclusions  
In order to improve the accuracy and speed of the optical biological parameter detection system for the human eye, this paper proposes a pupil target detection method based on improved YOLOv8n. Firstly, a dual attention mechanism is adopted, which not only focuses on the global contextual information of input data but also reduces the dependence on specific regions, improving the feature fusion ability and robustness of the model. Secondly, introducing dilated convolutions to replace ordinary convolutions in the original network improves the detection speed of the model without sacrificing detection accuracy. Finally, using Focaler-IoU as the damage function, by focusing on different regression samples, the performance of detectors in different detection tasks can be improved, resulting in improved bounding box regression performance of the detection model. The experimental results of the improved model on the CASIA-Iris dataset show that, compared with the original YOLOv8n, the method proposed in this paper has improved average detection accuracy and speed. The performance of the improved YOLOv8n algorithm was 0.99971, 1, 0.99611, and 0.96495 in precision, recall, MAP50, and mAP50-95, respectively. Moreover, the improved YOLOv8n algorithm reduced the model parameters by 7.18% and the computational complexity by 10.06%. The average processing time of each image was 18.37 ms. Compared with some mainstream object detection methods at present, this  
Figure 16: Location algorithm based on gradient means: (**a**) Traditional radial symmetry transformation algorithm. (**b**) Location algorithm based on gradient means. + is the artificial marking of the center of the pupil; + and + are the algorithm locates the center point of the pupil.  
Figure 17: Images in bright light.  
algorithm also achieved certain advantages in several evaluation indicators, and it was able to effectively perform the object detection task for human pupils. Although the improved algorithm proposed in this paper can realize the recognition of the pupil and iris in the presence of eyelashes, eyelids, occlusions, and other circumstances, the effect of strong light on human eye recognition will be the focus of future research work.  
Conceptualization, [PERSON] and J.W.; methodology, [PERSON] and H.W.; software, K.X. and H.W.; validation, K.X. and H.W.; formal analysis, K.X.; investigation, K.X.; resources, K.X. and H.W.; data curation, K.X. and H.W.; writing--original draft preparation, K.X.; writing--review and editing, K.X. and H.W.; visualization, K.X.; supervision, K.X. and J.W.; project administration, K.X.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Provincial Science and Technology Development Program Key R&D Project (20210203156 SF), the Jilin Province Key Science and Technology Research and Development projects (20180201025 GX), and the Jilin Provincial Department of Education's 13 th Five-Year Plan Science and Technology Project (JJKH20190599 KJ).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
Figure A1: Cont.