Interference (communication)

Algorithm

Linguistics

Process (computing)

Engineering

Philosophy

Stability (learning theory)

Operating system

Telecommunications

Computer science

Surface (topology)

Computer network

Point cloud

Mathematics

Feature (linguistics)

Artificial intelligence

Geometry

Point (geometry)

Channel (broadcasting)

Machine learning

Line (geometry)

Computer vision

Frame (networking)

Measurement

Based on intensive study on rail surface defect, a rail surface defect inspection scheme is proposed in this paper. With high-precession structured laser sensors, the original data are collected by industrial control computer and transformed into dimensional point cloud pattern. According to analysis of the specific feature of rail surface, a registration method is proposed to re-construct the digital rail surface. To achieve a high-precision detection result, a novel curve fitting model is established and accordingly a dynamic defect detection algorithm is improved to adapt to the successive production process based on the former study. Within the detailed study process, for each single frame data, a judge method is developed for noise elimination and specific feature defect detection. Finally, by studying successive frames of data points (actually cloud points), multiple frames data are dynamically computed to judge if there is a defect area on rail surface. In this paper, to avoid the random influence of minor factors, a probability threshold is introduced to judge defect points in the algorithm, which increases the anti-interference and decreases instability in the defect detection process. To verify the effect of proposed method, an experimental bench is developed in research process. Experimental results show that the detection method has reliable stability and anti-interference possibly influenced by minor factors like oxygen layer detection and characters area detection on rail surface. Compared with traditional two dimensional defect detection methods, the algorithm proposed in this paper is less computationally intensive and more suitable for online detection on rail surface.

Defect detection method for rail surface based on line-structured light