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Objective The main defects on metro tunnel inner wall are cracks and water seepage. Current detection methods based on manual and semi-automatic equipment have problems such as high intensity, low efficiency, and low reliability. The intelligence-based detection/identification algorithm and detection system should be studied to realize informative and intelligent detection for metro tunnel lining defects. Method The current status of metro tunnel defect inspection technology is analyzed, and a set of algorithms applicable to the identification of metro tunnel lining defects are put forward, mainly including image processing algorithm, defect category detection algorithm, and defect grading detection algorithm, etc. Four indexes are chosen to evaluate the detection effect of the identification algorithm. Furthermore, an intelligent detection system for metro tunnel lining defects based on deep learning method is established from both software and hardware aspects. Finally, the system is implemented on Beingjing Metro Line 3 to analyze the reliability of its on-site application. Result & Conclusion After the application of the intelligent detection system, the detection rate of metro tunnel lining crack defects reaches 91.95% with a false detection rate of 0.89%. The detection rate of seepage defect is 93.83% and the false detection rate 0.65%. This system can be used as the core intelligent detection platform of metro tunnel, effectively detecting various defects in metro tunnel.