# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 1 Introduction  
Pupil localization plays an important role in identity verification, disease recognition, visual focus of attention (VFOA) tracking, cognitive assessment for dementia, and detection of driving fatigue [1]. However, factors such as poor image quality and occlusion caused by eyelids, eyelashes, and blinking, as well as the pupil shape not being a perfect circle [2], have led to a decrease in the accuracy and robustness of pupil center localization, making it still challenging to achieve high-precision and real-time localization. Existing research has introduced numerous pupil localization detection techniques, both domestically and internationally. These encompass methods such as gradient vectors, Hough circle transform, ellipse fitting, and other traditional image processing techniques [3; 4; 5; 6; 7; 8]. [PERSON] et al. made use of gray-level details from human eyes [9], positioning the eye area via a gray-level projection function. This approach boasts low computational requirements but comes with lower localization precision. [PERSON] et al. [10] proposed a refined pupil recognition algorithm based on Hough circle transform. This advancement attained pupil center localization by confining the detection radius after conducting Canny edge detection and employing Hough transform to determine the optimal fitting circle. This advancement tackles the high computational complexity and poor real-time performance of traditional Hough transform methods, yet it necessitates a resetting of radius parameters, leading to increased manual dependency with the change in image datasets. [PERSON] et al. [11] introduced a swift radial symmetry transform algorithm, essentially refining Hough transform by transitioning the mapping space from parametric space todigital images, thus successfully reducing the algorithm's transformation dimensions and addressing the issue of increased algorithmic complexity due to the high dimensionality of Hough transform's parametric space, which often hinders real-time performance. With the advancement and refinement of deep learning techniques, their application in fields such as image classification, segmentation, and object tracking has yielded outstanding outcomes. In pupil localization, researchers have employed convolutional neural networks (CNNs) and the YOLO series networks. Instances include the utilization of eye images as inputs for CNNs to directly predict pupil centers in [12; 13; 14; 15], and pupil localization via the YOLOv3 model coupled with the starburst ray method in [16]. boosted-oriented edge optimization (BORE) [17] works as a booster algorithm that can be used to increase the accuracy of classification and regression problems. BORE algorithm-based pupil localization methods operate beyond 500 Hz [18]. Dealing with low-quality VS eye images is extremely difficult. However, the symmetric and expansive structure of U-Net helps achieve precise pupil localization while maintaining contextual information. Moreover, it generates greater accuracy in pupil localization for highly challenging VS eye images while letting the algorithm run in real time [19; 20; 21]. Compared to traditional approaches, object tracking networks exhibit notable improvements but are often less robust and may result in fluctuations in pupil center positions. Deep learning methods extract deep semantic information from images, known as deep features [22; 23]. Their essence lies in transforming target recognition into a regression task. For images under varying conditions, including noise, these methods provide rapid target recognition while maintaining high detection accuracy, thereby demonstrating overall excellence. Compared to traditional methods, deep learning offers superior expression and representation of targets within images. Furthermore, in scenarios involving changes in target shapes, lighting conditions, or occlusions, deep learning-based object detection algorithms display greater robustness.