自然环境下路面裂缝的识别

Road Crack Detection under Natural Environment

论文提出了一种基于分数阶微分和图像形态学的路面裂缝检测算法。分数阶微分能有效增强信号中、高频部分,非线性保留信号的低频部分,通过构建分数阶微分掩模算子,增强裂缝信息特别是平滑区域中弱信号信息。利用图像形态学算子提取裂缝,通过组合中值滤波去除孤立噪声点。实验结果表明,该算法比传统算法能更有效地检测出细小裂缝信息,是一种具有较强鲁棒性且高效实用算法。

A new algorithm of road crack detection based on fractional differential and image morphology is proposed.Fractional differential can effectively enhance high-frequency signals,medium-frequency signals and non-linearly reserved low-frequency signals.After fractional differential covering module is constructed and applied to road images,road crack signal especially weak signal in smooth area reinforcement is implemented.Then in order to filter isolated noise and detect crack,enhanced image is operated by image morphology operators and a group of median filters.Experimental results proved that the algorithm is more robust and effective to detect road cracks especially for exiguous cracks than traditional algorithms.