We propose a stereoscopic object super-resolution neural network (SSRNet) model based on pose estimation. The model uses a multi-task parallel network architecture to perform structural analysis and morphological feature extraction on low-resolution target images. The structural analysis estimates the target's pose and segments its components, while the morphological features are used to reconstruct the super-resolution image. This approach addresses the issue of varying target appearances under different poses, and fully utilizes a priori knowledge of target morphology, and achieves high-precision super-resolution image reconstruction as well as the identification and localization of key components and key points. This provides a new and effective technical approach for high-precision detection, recognition, and key component inspection of long-range stereoscopic small targets.