SODSR: A Three-Stage Small Object Detection via Super-Resolution Using Optimizing Combination

Face detection is a fundamental task in computer vision, yet remains challenging in educational settings due to the presence of objects of various sizes. Subpar detection can significantly impede subsequent tasks' performance. To address this, we present a novel framework, Small Object Detection Super Resolution (SODSR), inspired by super resolution (SR) techniques for feature-level images. SODSR comprises three stages: (1) Constructing a 3D model evaluation matrix to select optimal model combinations based on detection accuracy and image quality metrics. (2) Employing Double-thread FDN in the first stage to preprocess images, enhancing feature resolution for potential facial objects. (3) Leveraging Multi-head HyperNet in the second stage to augment face feature detection and improve accuracy. Finally, in the third stage, we introduce AFPGAN, a facial prior feature enhancement network, coupled with StyleGAN2 for texture and contour detail enhancement. Experimental results demonstrate that SODSR outperforms existing small object detection (SOD) models in both accuracy and visual fidelity.