当前的直线段检测算法均针对灰度图像设计和实现,在处理彩色图像时会抛弃其色彩信息,不利于获取高质量检测结果。为此,提出了一种针对彩色图像的直线段检测算法。对三个色彩通道分别应用DiZenzo算子计算出其梯度矢量,并基于平均梯度强度和方向提取出图像中的边缘;再对边缘线段上的像素点进行跟踪、链接或分裂,以生成候选的直线段。最后,基于候选直线段上各点的梯度信息,利用Helmholtz准则排除由噪声形成的虚假直线段,从而得到最终的直线段集合。实验结果表明,与现有算法相比,新算法充分应用了图像的色彩信息,显著提高了直线段检测的性能,在York城市图像数据库上可将当前算法的最高精确率从0.2207提升为0.2687,并获得了更高的F-得分。

The existing algorithms for detecting straight line segments are all designed for grayscale images. When implemented on color images, they discard the input image's color information; this is undesirable for accurately detecting straight line segments. To solve this problem, this study proposes an algorithm that directly detects straight line segments in color images. First, DiZenzo operator is utilized to compute gradient vectors in three color channels, and image edges are extracted based on the average gradient magnitudes and orientations. Then, the pixels on each image edge are tracked and they are linked or split to generate candidate straight line segments. Finally, based on the gradients of the points on the candidate line segments, Helmholtz criterion is used to eliminate the false line segments caused by noise, yielding the final set of line segments. Experimental results show that the new algorithm can fully exploit the color information of the input image, leading to significantly improved detection efficiency when compared with the existing algorithms. On the YorkUrbanDB image database, the highest accuracy of the proposed algorithm increases from 0.2207 to 0.2687 and a high F-score is achieved.