Increasing interest in a tile-paving robot calls for a robust tile detection algorithm. This paper proposes the Ultra Clear Tile (UC-Tile) algorithm to detect corners and edges and assist tile paving automation in positioning and installation tasks. UC-Tile is designed to incorporate deep learning for semantic segmentation with rule-based post-processing algorithms. The semantic segmentation algorithm investigated herein is a fine-tuned version of YOLOv8. UC-Tile mainly contributes to refitting the edges and locating the tile corners with tailored algorithms. A dataset is developed comprising 1486 images exhibiting varying patterns of tiles, captured under disparate heights and illumination conditions. Results indicate that UC-Tile outperforms common benchmark algorithms, and can achieve the highest mIoU 98.68 %, F1-Score 99.31 %, and the lowest 95-HD. Further, UC-Tile can accurately predict real distance and angles with small differences to ground truth values, thereby informing robotic movement control. This algorithm is expected to enable precise automatic tile paving.

中文翻译：

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结合深度学习和基于规则的计算机视觉算法的自动瓷砖位置和方向检测


人们对瓷砖铺路机器人的兴趣日益浓厚，这需要一种强大的瓷砖检测算法。本文提出了超透明瓷砖 （UC-Tile） 算法来检测角落和边缘，并协助瓷砖铺路自动化进行定位和安装任务。UC-Tile 旨在将深度学习与基于规则的后处理算法相结合，以实现语义分割。本文研究的语义分割算法是 YOLOv8 的微调版本。UC-Tile 主要有助于通过定制算法重新拟合边缘和定位瓦片角。开发了一个数据集，其中包含 1486 张图像，这些图像展示了在不同高度和照明条件下捕获的不同瓦片图案。结果表明，UC-Tile 的性能优于常见的基准测试算法，可以实现最高的 mIoU 98.68 %、F1-Score 99.31 % 和最低的 95-HD。此外，UC-Tile 可以准确预测与真实值差异很小的真实距离和角度，从而为机器人运动控制提供信息。此算法有望实现精确的自动切片铺路。