License Plate Recognition Under the Dual Challenges of Sand and Light: Dataset Construction and Model Optimization

License plate recognition in sandstorm conditions faces challenges such as image blurriness, reduced contrast, and partial information loss, which result in significant limitations in the feature extraction and recognition accuracy of existing methods. To address these challenges, this study proposes a license plate recognition method based on an improved AlexNetBN network. By introducing Batch Normalization (BN) layers, the model achieves greater training stability and generalization in complex environments. A dedicated dataset tailored for license plate recognition in sandstorm conditions was constructed, and data augmentation techniques were used to simulate real-world scenarios for model training and testing. Experimental results demonstrate that, compared to the traditional AlexNet model, AlexNetBN achieves higher recognition accuracy and robustness in environments with frequent sandstorms and significant variations in lighting intensity. This study not only effectively enhances license plate recognition performance under sandstorm conditions but also offers new insights and references for applying CNN-based methods in low-visibility scenarios.