帅 琦,董 昱.基于混合模型的钢轨检测识别方法[J].电子测量与仪器学报,2022,36(4):160-168 |
基于混合模型的钢轨检测识别方法 |
Rail detection and recognition method based on hybrid model |
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DOI: |
中文关键词: 钢轨识别检测 组合模型 线性近似 累计概率霍夫变换 B 样条曲线 |
英文关键词:rail recognition and detection combination model linear approximation progressive probabilistic Hough transform B-spline
curve |
基金项目:国家自然科学基金(61763023)项目资助 |
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中文摘要: |
为了改善我国现有钢轨检测识别方法准确性和鲁棒性不高以及弯轨拟合较差等问题,提出一种基于直线-曲线混合模
型的钢轨检测识别算法。 首先对图像进行预处理,调整滞后阈值进行 Canny 边缘检测。 采用累计概率 Hough 变换对直轨检测
并完成近远视场的划分以及消失点的确定。 对近视场直轨采用直线模型拟合,根据其检测结果对远视场进行循环线性近似获
取钢轨特征点,并根据钢轨灰度特征进行验证,采用最小二乘法完成曲线拟合。 直线-曲线模型的切换根据制定的规则完成。
实验结果表明,提出的算法检测正确率为 90. 1%,适用于不同环境的场景,具有较好的鲁棒性。 |
英文摘要: |
Aiming at the problems of low accuracy and robustness of rail detection and recognition methods and poor fitting of curved
rail, a rail detection and recognition algorithm based on straight-curve hybrid model is proposed. In the beginning, the image is
preprocessed and Canny edge detection is completed by adjusting the lag threshold. The Progressive Probabilistic Hough Transform is
used to detect the direct straight track, divide the near and far field of view and determine the vanishing point. The straight track in the
near field is fitted by a linear model, and the feature points of the rail are obtained by the circular linear approximation of the far field
according to the detection results, and verified according to the gray characteristics of the rail. The least square method is used to
complete the curve fitting. The switch of straight-curve model is completed according to the established rules. Experimental results show
that the detection accuracy of the proposed algorithm is 90. 1%, which is suitable for different environments and has good robustness. |
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