3D Histogram based Maximum Entropy Threshold Segmentation for Railway Fence Detection

We propose an automatic inspection method for detecting the railway fence defects based on train-borne line scan imaging. Once we get the whole fence images of continuous guardrail, the guardrail location of railway fence can be extracted as foreground by image segmentation algorithm; then we recognize the fence defect by the analysis of distance between the adjacent guardrails, based on the truth that the distance between any two normal guardrails is more or less fixed, while broken fence often causes the guardrail missing, which will cause the fixed distance change several-fold. We propose a maximum entropy thresholding segmentation based on three dimensional histogram MVG to realize the guardrail location, and the corresponding algorithm is also designed to analyze the range of guardrail interval for realizing the detection of defective fence. The experimental results show that the proposed algorithms not only perform well at the metal fence of general-speed ballast railway but also at the concrete fence of high-speed ballastless railway.