对比多种预处理方法，发现YUV转换能显著提升交通标志识别准确率。
Comparing Performance of Preprocessing Techniques for Traffic Sign Recognition Using a HOG-SVM
- 用HOG+SVM在GTSRB数据集上测试不同预处理方法
- YUV使分类准确率从89.65%提升至91.25%
- 适合做交通标志识别系统优化的开发者参考
本研究在德国交通标志识别基准数据集（GTSRB）上，使用方向梯度直方图（HOG）与支持向量机（SVM）比较了多种预处理技术对交通标志识别（TSR）性能的影响。评估的技术包括CLAHE、HUE和YUV。结果显示，采用YUV转换可显著提升HOG-SVM分类器的性能，准确率从89.65%提高到91.25%，为改进TSR系统的预处理流程提供了有效参考。
原文摘要 · Abstract (English)
This study compares the performance of various preprocessing techniques for Traffic Sign Recognition (TSR) using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Techniques such as CLAHE, HUE, and YUV were evaluated for their impact on classification accuracy. Results indicate that YUV in particular significantly enhance the performance of the HOG-SVM classifier (improving accuracy from 89.65% to 91.25%), providing insights into improvements for preprocessing pipeline of TSR applications.
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