Multisensor evidence integration and optimization in rail inspection

For safety purpose, railroad tracks must be inspected regularly for defects or other design non-compliances. One crucial building block in an automatic inspection system is to detect different types of railroad track objects. We introduce a novel global optimization framework to combine evidence from multiple cameras and the distance measuring instrument to improve rail object detection. Our framework leverages the cross-object spatial constraints enforced by the sequential structure of rail tracks, as well as the cross-frame and cross-view constraints in camera streams. Experimental results on real rail track-driving data demonstrates that our approach achieves superior performance compared to processing each data stream independently. We argue that our approach can be extended to other embodiments involving linear sequential structures, such as pipeline, highway and road inspection.