Railway Video Inspection System based on ACP Theory

In this paper, we introduce a design framework of railway inspection system based on ACP theory, which can effectively overcome the problems of real world data acquisition and model learning and evaluation by constructing virtual data sets with controllable virtual space and imaging conditions. The framework uses artificial virtual scenes to simulate the actual scenes representing complex challenges, and then combines the actual scene data set and the virtual scene data set through computational experiments to train and evaluate the visual model, and finally realizes the online optimization of the visual model through parallel execution of virtual and real interaction. The framework realizes the automatic detection of railway tracks, guardrail, sound barrier, power supply network and other objects, as well as the active detection of potential unsafe factors, effectively making up for the shortcomings of traditional railway video detection algorithms that cannot be adequately trained and evaluated due to insufficient data.