Condition monitoring of wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are to large extent based on visual analysis. In this paper a machine vision based approach has been considered to emulate the visual abilities of the human operator to enable automation of the process. Digital images from either ends (left and right) of the sleepers have been acquired. A pattern recognition approach has been adopted to classify the condition of the sleeper into classes (good or bad) and thereby achieve automation. Appropriate image analysis techniques were applied and relevant features such as the number of cracks on a sleeper, average length and width of the crack and the condition of the metal plate were determined. Feature fusion has been proposed in order to integrate the features obtained from each end for the classification task which follows. The effect of using classifiers like multi-layer perceptron and support vector machines has been tested and compared. Results obtained from the experiments show that multi-layer perceptron and support vector machines have achieved encouraging results, with a classification accuracy of 90%; thereby exhibiting a competitive performance when compared to a human operator.     

世界公路桥梁检测方法研究

美国运输委员会的主要目标之一是提高我们国家的道路安全.针对这一目标,联邦、州和地方交通机构一致认为检测我们的近600 000座桥梁是非常重要的.这些机构每年投入大量资金到桥梁检测方面的行动.确保质量,使得我们的桥梁检查计划维持在最高的水平,且资金的利用尽可能有效.通过美国桥梁检查计划收集的数据不仅用来确保我们的桥梁对公众来说是安全的,而且有助于构建编制桥梁养护、维修、复原和更换的基础.因此,确保高质量的数据收集是在检测过程中进行的,这是极其重要的.