用AI图像技术自动计票，提升选举透明度与可信度。
Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning
- 结合OpenCV、CVZone和MOG2算法实现自动计票
- 通过F1分数验证系统准确率显著优于人工计票
- 适合关注选举透明化与技术治理的研究者
为解决选举过程中人工计票存在的问题，本研究探讨了利用先进图像处理技术实现自动化选票统计的可行性。研究聚焦于OpenCV、CVZone及MOG2算法在自动化系统中的应用，表明此类技术可大幅提升选举流程的效率与透明度。实证结果表明，自动化计票能有效优化投票过程，并在信任度较低地区重建公众对选举结果的信心。研究强调需采用严格的评估指标（如F1分数）系统比较自动化系统与人工计票的准确性，从而实现对两种方法性能差异的精细化评估。该方法构建了全面的评估体系，保障了自动化投票系统在选举领域的合法性与可靠性。
原文摘要 · Abstract (English)
In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase the effectiveness and openness of electoral operations. The empirical findings demonstrate how automated voter counting can enhance voting processes and rebuild public confidence in election outcomes, particularly in places where trust is low. The study also emphasizes how rigorous metrics, such as the F1 score, should be used to systematically compare the accuracy of automated systems against manual counting methods. This methodology enables a detailed comprehension of the differences in performance between automated and human counting techniques by providing a nuanced assessment. The incorporation of said measures serves to reinforce an extensive assessment structure, guaranteeing the legitimacy and dependability of automated voting systems inside the electoral sphere.
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