Chess position identification using pieces classification based on synthetic images generation and deep neural network fine-tuning

• Rafael Campello UFPE
• Afonso Delgado UFPE

Resumo

Chess pieces recognition using computer vision is a problem generally approached in various ways, with different kinds of results and complexity. Deep learning is a state of the art approach to solve problems on image recognition although facing necessity of huge data sets. This paper discusses a method to identify synthetically generated chess images on Blender using its Python API via fine-tuning of VGG16 convolutional network obtaining close to 97% accuracy on piece classification. Possible applications include automated record of real chess games and real-time play between online players using real boards.

Palavras-chave: chess, neural networks, piece recognition, synthetic data generation