Linear Array Image Alignment Under Nonlinear Scale Distortion for Train Fault Detection

In pushbroom-style train imaging systems, the efficiency and accuracy of image alignment are crucial for improving train fault detection accuracy. However, nonlinear scale distortion in linear array images poses significant challenges to alignment precision. To address this, our study introduces an innovative image alignment algorithm for linear arrays, adept at handling nonlinear scale distortions. This algorithm is particularly effective in aligning heterogeneous images, even with substantial differences in texture features. The developed dynamic step-length sliding window strategy, feature point matching using geometric constraints, polynomial-constrained outlier elimination, and interval feature matching fusion significantly enhance both the accuracy and density of feature point matching. Furthermore, the application of the weighted radial basis function (WRBF) facilitates precise coordinate transformation in the image remapping process. Comprehensive experimental evaluations demonstrate the algorithm's superior alignment precision and efficiency in both homogenous and heterogeneous image alignment scenarios, markedly boosting train fault detection accuracy. The algorithm's versatility extends its utility beyond train fault detection to broader applications in pushbroom-style imaging system alignment tasks.