A Subpixel-level Parallel Alignment Technology Oriented to EMU Train Undercarriage Image.

In order to realize efficient fault recognition and utilization of the high-speed train (EMU) undercarriage image obtained by detection robot, image alignment technology play an important role. Firstly, research the original image data collection process of the EMU fault detection robot in the practical working site; secondly, perform image enhancement such as gamma transformation and Hanning window adding on the original image; thirdly, compared with the image alignment algorithm based on feature extraction, a Fourier transform-based image alignment algorithm is proposed; finally, the affine processing with global transform parameters acquired from image alignment is given. This paper conducts experimental verification and comparative analysis, and selects a convolutional neural network (CNN) model based on deep residual structure to carry out this experiment. Through comparison with three typical image alignment algorithms based on feature extraction, it shows that the Fourier transform-based image alignment algorithm has significantly improved in the accurate alarm rate, mistaken alarm rate, and omitted alarm rate with strong robustness. Based on the intrinsic subpixel-level precision of domain transformation alignment algorithm, and the parallel image processing method proposed in this paper, the on-site image preprocessing, high-precision alignment, and automatic fault recognition can be realized stably and reliably within a reasonable time.