Automated status inspection of fastening bolts on freight trains using a machine vision approach

The status inspection and maintenance actions on the mechanical components of freight trains are significant determinants of railway safety. Fastening bolts, as a key component, are widely used to transmit, connect and fasten various parts of freight trains. Their absence can degrade the original structure and lead to accidents. Recently, machine vision approaches have been widely used to inspect the status of mechanical components, thereby reducing costs and avoiding traffic accidents. This paper proposes a visual inspection system that is based on the machine vision approach and can be used to automatically inspect the status of fastening bolts on freight trains. To detect the presence/absence of a fastening bolt in a complex background, a hierarchical detection framework consisting in fault area extraction and fastening bolt detection is proposed. In the first module, a gray projection method is used to divide the fault area that contains the target from the complex background. Subsequently, a fastening bolt detector is designed to verify the candidate image regions. Several gradient-orientation-based features and a classifier can be used to perform the detection task. Experimental results show that the combination of a gradient orientation co-occurrence matrix and a support vector machine has the best classification performance. The proposed inspection system has the advantages of good real-time performance and high inspection accuracy; it achieves an accuracy of 99.96% with a speed of nine frames per second.