Detection and analysis of key component of train's locomotive based on image analysis techniques

With the fast development of Chinese high-speed railway, the operation safety of locomotives has attracted great attention of railway departments. As an important detection aspect of locomotive safety, the locomotive bottom has lot of insecurity factors, and faults of running gear are the most direct factor affecting the safe operation of locomotives. Generally, the traditional method is extremely inefficient, and manpower cannot allow wayside dynamic detection. In order to improve the effectiveness of detection, and at the same time allow dynamic detection of key components at the locomotive bottom, the research on effective detection and identification schemes of locomotive bottom faults is promising. As for the whole detection and recognition system of locomotive bottom faults, this paper mainly studies detection of running gear deformation, damage, omission, and the presence of foreign objects and other faults based on the image comparison method. Increasing the detection rate and reducing the false detection rate are the main purpose of this project. This paper used multi-feature fusion method for process the detection data of the running gear, which combined the image edge detection and visual similarity method, comprehensively considered the edges, contrast, brightness, and structural characteristics of the image, finally improved the detection rate and reduced the false detection rate.