铁路扣件图像边缘检测中LoG参数优化

边缘特征是铁路扣件缺陷分类的重要依据。高斯拉普拉斯（LoG）算子提取的边缘特征最接近扣件的真实边缘，但LoG参数的选取直接影响边缘检测效果。针对LoG参数的优化，采用基于布谷鸟搜索优化高斯拉普拉斯边缘检测的算法。利用布谷鸟优化搜索对阈值邻近像素值和标准偏差参数进行优化来提高拉普拉斯边缘检测性能。利用Pratt品质因数IMP值判定检测到的边缘是否最佳。实验证明，该方法有效优化了LoG参数的设置，得到的边缘特征提高了扣件缺陷分类准确率。

Optimization of LoG parameters in railway fastener edge detection system

Edge character is the important basis of rail fastener defect classification. The edge extracted by LoG operator is most close to the real edge of fasteners, but the effect of edge detection is directly affected by the selection of LoG parameters. In view of the LoG parameter optimization, optimizing the parameter of LoG algorithm based on the cuckoo search optimization is adopted. First of all, adjacent pixel threshold value and standard deviation parameters are optimized based on the cuckoo optimize search operator to improve the edge detection performance of LoG operator. Then, the quality factor of Pratt IMP value is used to judge whether the edge character is the best one. Finally, the experiments show that this method is effective to optimize the parameters of the LoG settings. The accuracy of fastener defect classification is improved based on the improved edge character.