Chess Board State Detection and Applications through Artificial Intelligence and Machine Learning, and Convolutional Neural Networks
DOI:
https://doi.org/10.58445/rars.1270Keywords:
Chess, Artificial Intelligence, Machine Learning, Image Processing, Neural Networks, Computer Science, Recognition Systems, Software, CodingAbstract
This paper addresses the enhancement of automated chess recognition systems,
focusing on overcoming challenges such as poor lighting, diverse camera angles, and varying
chess set designs. Through the use of cutting-edge convolutional neural networks (CNNs)
trained on diverse datasets, this study employs advanced image processing techniques
including edge, corner, and line detection algorithms, along with the RANSAC algorithm for
robust corner identification. Pretrained CNN models are used for classifying board occupancy
and piece types. The methods significantly improve system accuracy under controlled
conditions, demonstrating high success rates in LED lighting on homogeneous surfaces.
However, performance is still affected by extreme lighting variations, unconventional chess sets,
non-standard camera angles, and glare reflected by the chessboard. The study presents
promising advancements, highlighting the potential for further improvements to enhance
universal applicability and robustness in real-world scenarios.
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