Musical instrument classification method based on data augmentation and computer vision

With the rapid development of computer vision and deep learning, musical instrument image recognition has gradually become an important research direction in music education, intelligent retrieval, and multimedia content management. However, instrument images are often affected by varying lighting conditions, shooting angles, and complex backgrounds during actual acquisition, posing challenges to the generalization capability and robustness of models. To address this issue, this paper proposes an instrument image classification method combining data augmentation and a lightweight deep learning model. To tackle the diversity in shooting angles, lighting conditions, and background complexity of instrument images, we employ diverse data augmentation strategies (including random cropping, horizontal flipping, rotation, color perturbation, etc.) to enhance the model's robustness and generalization ability. Using the Kaggle musical instrument classification dataset as the experimental subject, we adopt MobileNetV2 as the lightweight convolutional neural network to construct an end-to-end classification framework. After 80 training epochs, the proposed method achieves a high classification accuracy of 88% on the validation set, significantly outperforming the baseline accuracy (80%), demonstrating the effectiveness of combining data augmentation with a lightweight network. This study provides a feasible solution for instrument recognition in music education, intelligent retrieval, and multimedia content management.