R-CNN framework accurately recognizes chess positions from images

Researchers have developed a novel R-CNN-based framework for recognizing chess positions from single images. This system independently handles piece recognition and board geometry estimation, combining their outputs for a complete position reconstruction. The adapted Faster R-CNN model, enhanced with a class-weighted objective and a deeper classification head, significantly improves piece detection accuracy. Additionally, Mask R-CNN is utilized to predict keypoints for board orientation and homography estimation, which maps piece locations to an 8x8 grid, leading to highly accurate square assignments and overall position recovery. AI

IMPACT This research advances computer vision techniques for object recognition and spatial mapping, potentially applicable to other domains requiring precise visual analysis.

RANK_REASON The cluster contains an academic paper detailing a new method for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• DagsHub
• Faster R-CNN
• Gotit.pub
• Hugging Face
• Mask R-CNN
• Ognjen Arandjelović
• R-CNN

• paper
• product

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