Pixel

Engineering

Aerospace engineering

Materials science

Grayscale

Range (aeronautics)

Computer science

Subtraction

Surface (topology)

Scan line

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Artificial intelligence

Geometry

Noise (video)

Consistency (knowledge bases)

Background subtraction

Line (geometry)

Computer vision

Arithmetic

IEEE Transactions on Instrumentation and Measurement

Computer vision systems have attracted much attention in recent years for use in detecting surface defects on rails; however, accurate and efficient recognition of possible defects remains challenging due to the variations shown by defects and also noise. This paper proposes a coarse-to-fine model (CTFM) to identify defects at different scales. The model works on three scales from coarse to fine: subimage level, region level, and pixel level. At the subimage level, the background subtraction model exploits row consistency in the longitudinal direction, and strongly filters the defect-free range, leaving roughly identified subimages within which defects may exist. At the next level, the region extraction model, inspired by visual saliency models, locates definite defect regions using phase-only Fourier transforms. At the finest level, the pixel subtraction model uses pixel consistency to refine the shape of each defect. The proposed method is evaluated using Type-I and Type-II rail surface defect detection data sets and an actual rail line. The experimental results show that CTFM outperforms state-of-the-art methods according to both the pixel-level index and the defect-level index.

A Coarse-to-Fine Model for Rail Surface Defect Detection