To address the challenge of accurately reconstructing micro-defects on highly reflective surfaces during in-service inspection of aero-engine blades, a three-dimensional (3D) reconstruction method based on a binocular endoscopic system is proposed. To overcome the limitations of insufficient calibration accuracy in endoscopic scenarios, a concentric dual-ring calibration target is designed, and a feature point extraction and optimization algorithm based on concentricity constraints is developed to achieve high-precision binocular calibration. Experimental results demonstrate that the proposed method achieves average single-camera reprojection errors of 0.095 and 0.103 pixels, respectively, while significantly reducing binocular calibration error and improving the accuracy of system geometric parameters. For stereo reconstruction, a deep learning-based detection model, YOLO11, is integrated to automatically locate defect regions and obtain prior information of the detection bounding boxes in both left and right views. A region-constrained and prior-disparity filtering strategy based on the detection boxes is proposed, which determines the intersection of the corresponding epipolar search regions and disparity ranges. This approach confines the matching computation to the defect areas, preventing the propagation of smoothing costs from incorrect matches outside the detection boxes, thereby enhancing stereo matching stability and local reconstruction accuracy. Moreover, the AD-Census stereo matching algorithm is adaptively modified to further suppress noise. The reconstructed point clouds of scratches and pits exhibit relatively uniform density. Measurement results indicate that the relative errors of pit diameter and scratch length are both less than 1%, and the depth measurement error does not exceed 8%, meeting engineering accuracy requirements. The proposed method demonstrates strong robustness and precision under complex illumination and spatially constrained conditions, outperforming conventional approaches in terms of calibration accuracy, local reconstruction quality, and measurement reliability. This work provides an effective technical foundation for high-precision 3D surface morphology measurement of in-service aero-engine blades.