FDNet: A Novel Image Focus Discriminative Network for Enhancing Camera Autofocus

Accurate activation and optimization of autofocus (AF) functions are essential for capturing high-quality images and minimizing camera response time. Traditional contrast detection autofocus (CDAF) methods suffer from a trade-off between accuracy and robustness, while learning-based methods often incur high spatio-temporal computational costs. To address these issues, we propose a lightweight focus discriminative network (FDNet) tailored for AF tasks. Built upon the ShuffleNet V2 backbone, FDNet leverages a genetic algorithm optimization (GAO) strategy to automatically search for efficient network structures, and incorporates coordinate attention (CA) and multi-scale feature fusion (MFF) modules to enhance spatial, directional, and contextual feature extraction. A dedicated focus stack dataset is constructed with high-quality annotations to support training and evaluation. Experimental results show that FDNet outperforms mainstream methods by up to 4% in classification accuracy while requiring only 0.2 GFLOPs, 0.5 M parameters, a model size of 2.1 MB, and an inference time of 0.06 s, achieving a superior balance between performance and efficiency. Ablation studies further confirm the effectiveness of the GAO, CA, and MFF components in improving the accuracy and robustness of focus feature classification.