Real-Time Braille Image Detection Algorithm Based on Improved YOLOv11 in Natural Scenes

The development of Braille recognition technology is intrinsically linked to the educational rights of individuals with visual impairments. The key challenges in natural scene Braille detection include three core trade-offs: difficulty extracting small-target features under complex background interference, a balance between model accuracy and real-time performance, and generalization across diverse scenes. To address these issues, this paper proposes an improved YOLOv11 algorithm that integrates a lightweight gating mechanism and subspace attention. By reconstructing the C3k2 module into a hybrid structure containing Gated Bottleneck Convolutions (GBC), the algorithm effectively captures weak Braille dot matrix features. A super-lightweight subspace attention module (ULSAM) enhances the attention to Braille regions, while the SDIoU loss function optimizes bounding box regression accuracy. Experimental results on a natural scene Braille dataset show that the algorithm achieves a Precision of 0.9420 and a Recall of 0.9514 with only 2.374 M parameters. Compared to the base YOLOv11, this algorithm improves the combined detection performance (Precision: 0.9420, Recall: 0.9514) by 3.2% and reduces computational complexity by 6.3% (with only 2.374 M parameters). Ablation experiments validate the synergistic effect of each module: the GBC structure reduces the model parameter count by 8.1% to maintain lightweight properties, and the ULSAM effectively lowers the missed detection rate of ultra-small Braille targets. This study provides core algorithmic support for portable Braille assistive devices, advancing the technical realization of equal information access for individuals with visual impairments.