In precision measurement tasks such as augmented reality (AR) displays, laser beam calibration, and diffractive optical element inspection, the sub-pixel localization accuracy of spot centers is critical for system stability and reliability. Conventional positioning methods based on model fitting or rule extraction struggle to achieve robust and accurate spot center estimation in complex scenarios due to system aberrations, diffraction distortions, and structural noise. To address these limitations, a U-Net-based spot center localization framework is proposed. This framework incorporates an adaptive dual-channel heatmap supervision mechanism to simultaneously model the spot center and its diffusion structure, enabling precise characterization of real-world point spread function (PSF) features. During inference, a Hessian matrix-based second-order differential extremum detection method is employed to enhance the stability of sub-pixel peak localization. Experimental results demonstrate that the proposed method exhibits a concentrated error distribution on real optical datasets, achieving an RMSE of 0.413 pixel. Compared with HRNet and FFD, the proposed method significantly improves both localization accuracy and stability.