基于细胞神经网络的公路裂痕诊断研究

Study on highway crack diagnosis based on cellular neural network

研究道路裂痕的诊断问题,交通安全系统中准确检测道路质量是保证安全性的关键。但现有的道路监测系统凸显出更多的缺点。为了更好的监测公路裂痕,提出了一种改进的细胞神经网络道路细微裂痕图像识别方法。该方法通过一定的图像处理,建立裂痕网络和细节网络,同时,增加了细微裂痕相似网络模型,避免了仅对裂痕特征提取信息不能准确识别细微裂痕的问题。实验证明,改进的裂痕识别算法实现简单,识别道路上的细微裂痕准确率高,达到了实时识别技术的要求。

The study on detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. But the current road detect system had appeare~ a lot ot shortages. In order to solve the above problem, this paper comes up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.