Coin classification automatically plays important roles in many applications, e.g., vending systems. Glossy reflection is one of the key factor that affect the performance of vision-based coin classification, especially in a complex environment. In this paper, we propose a novel method for robust coin classification. Contrary to the previous method, we get the glossy area first. Edge features and texture features are used in glossy area detection. Then the deep learning features are extracted based on non-glossy area instead of the whole coin image. Finally, the coin classification results are got from the VGG nets scheme. Comprehensive experiments show that our method is robust under various complex environments. The comparison experiments demonstrate that our method can outperform the state-of-the-art method. Our method achieves 95.80% accuracy.
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