基于改进Canny算法的道路标识检测方法研究

Research on Road Sign Detection Method Based on Improved Canny Algorithm

随着自动驾驶技术迅速发展,道路标识检测越来越重要。为解决当前道路标识检测中存在的边缘缺失和环境适应性差等问题,通过改进传统道路标识检测算法并在OpenCV平台上进行验证。首先,在图像处理阶段采用改进的双边滤波代替高斯滤波去除噪声保留边缘信息;其次,使用Scharr算子的x、y、45°和135°等4个方向计算图像梯度幅值;最后,针对Canny算法选取阈值自适应差、选择困难等问题,运用最大类间方差法(Otsu)进行阈值分割。在道路标识图像的实验表明,改进Canny算法相较于其他传统算法在单边缘响应方面表现更好,具有更高的边缘检测准确性和鲁棒性,处理时间相对较短。

With the rapid development of autonomous driving technology,road sign detection is becoming increasingly important.To address the issues of edge loss and poor environmental adaptability in current road sign detection,traditional road sign detection algorithms were improved and validated on the OpenCV platform.Firstly,in the image processing stage,an improved bilateral filter is used instead of Gaussian filtering to remove noise and preserve edge information;Then,use the Scharr operator to calculate the gradient amplitude of the image in four directions:x,y,45°,and 135°;Finally,to address the issues of poor threshold adaptation and difficulty in selecting thresholds for the Canny algorithm,the maximum inter class variance(Otsu)method is used for threshold segmentation.Experiments on road sign images have shown that the improved Canny algorithm performs better in single edge response compared to other traditional algorithms,with higher edge detection accuracy and robustness,and relatively shorter processing time.