Abstract: A DoFP polarization camera-based FSV polarization detection method is proposed in order to enhance the quality inspection of annular forging surfaces. An improved WGAN generating adversarial network is utilized to generate S-parametric maps to reduce the successful FSV polarization detection, which theoretically must be achieved by two acquisitions, to merely one acquisition. By constructing an experimental platform for polarization detection, an S-parameter polarization image dataset is established and the WGAN neural network is trained. Through the experimentally generated polarization data, an enhanced 2D surface image is obtained and the corresponding 3D modeling of polarization is performed. The proposed method enables simultaneous self-calibration of the fast-axis angular and phase delay quantities of LCVR, which greatly facilitates industrial applications. The experimental results present that the DoP images measured by the proposed detection method are improved by more than 3% in image evaluation index compared with the conventional imaging system and non-FSV polarization imaging system, which greatly improves the quality inspection capability of the ring forging surface.