A VGG16 transfer learning model integrating attention mechanism for road sign recognition

To address the limitations of traditional road sign detection methods in complex environments, this study proposes a road sign detection and recognition approach based on the VGG16 transfer learning model integrated with an attention mechanism. Traditional methods rely on image processing techniques and are prone to interference from factors like lighting variations and obstructions. With the advancement of deep learning technology, convolutional neural networks have achieved breakthroughs, offering new solutions for road sign recognition. This paper innovatively incorporates transfer learning to migrate features from general datasets to specific tasks, while adding an attention mechanism module to the VGG16 network to enhance the model's focus on road sign regions. Experimental results demonstrate that compared to mainstream image classification methods, the proposed method increases detection accuracy by 5.8% in complex scenarios, significantly improving the model's ability to recognize road signs.