In this paper, we present a processing method to detect millimeter interlayer debondings from Ground Penetrating Radar (GPR) B-scan images. The method is matched to carry out rapid debonding detection at the operational level. A machine learning based outlier-detection strategy namely, One-class Support Vector Machines (OCSVM) is proposed to detect A-scan data vectors which differ from a reference data set collected over a known healthy pavement area.OCSVM is tested on both simulated and experimental data representing GPR data over various artificial millimetric debondings at 2.6 GHz and 4.2 GHz from respectively ground-coupled and air-coupled radar configurations. The experimental data were collected at the Accelerated Pavement Test site located in the Nantes campus of Université Gustave Eiffel. The simulated models on the other hand were generated using a numerical EM solver based on Finite Difference Time Domain (FDTD) method namely, GprMax. Simulation tests allow to conduct sensitivity analysis to determine the robustness of the detection method at various signal-to-noise ratios (10 dB to 60 dB).The proposed OCSVM method demonstrated high performance on both simulated and experimental data to detect thin interlayer debondings over various GPR configurations.