Checkerboard check method based on deep learning semantic segmentation

The invention relates to a checkerboard inspection method based on deep learning semantic segmentation; the method is used for checkerboard inspection during calibration of any camera; a checkerboard image is firstly acquired to achieve the purpose of calibrating internal and external parameters of the camera, and the image is acquired through a vehicle-mounted camera and checkerboard inspection is performed. Compared with a traditional image processing method, the invention is high in accuracy and small in error degree, can be combined with the advantages of traditional checkerboard detection, improves the adaptability to interference items in the environment, and also improves the adaptability to environment illumination. 本发明涉及一种基于深度学习语义分割的棋盘格检查方法，所述方法用于任何相机标定时检测棋盘格用，首先获取棋盘格图像，以达到标定相机的内外参的目的，通过车载摄像头以获取图像并进行棋盘格检查。相对于传统的图像处理方法，本申请请准度高、误差度小，可以结合传统棋盘格检测的优点，提升对环境中干扰项的适应能力，同时也提升了对环境光照的适应能力。