The sunspot drawings around the globe provide long historical records for understanding the long-term trends in solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named as SPR-Mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPR-Mask consists of three parts: backbone, shared head and mask branch. Especially, it adopts a scale-aware attention network (SAAN) and a PointRend module in the mask branch to improve the accuracy of target edge segmentation. Besides that, each sunspot belonging to northern or southern (N-S) hemisphere is determined by transforming its cartesian coordinates to spherical coordinates after extracting P , B0 and L0 handwritten in sunspot drawings using a revised Lenet-5 deep learning method. The precision, recall and AP of SPR-Mask are 0.92, 0.93, and 0.92, respectively. The test results show the SPR-Mask method has a good performance. The numbers and areas of pores, spots, umbrae and penumbrae for N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelhöhe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show they have similar trends, high correlations and similar N-S asymmetries. All data of YNAO are public shared at https://github.com/yzs64/YNAO sd/, which are abundant complementary to the other sunspot catalogues in the world.

The sunspot drawings around the globe provide long historical records for understanding the long-term trends in solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named as SPR-Mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPRMask consists of three parts: backbone, shared head and mask branch. Especially, it adopts a scale-aware attention network (SAAN) and a PointRend module in the mask branch to improve the accuracy of target edge segmentation. Besides that, each sunspot belonging to northern or southern (N-S) hemisphere is determined by transforming its cartesian coordinates to spherical coordinates after extracting P, B0 and L0 handwritten in sunspot drawings using a revised Lenet-5 deep learning method. The precision, recall and AP of SPR-Mask are 0.92, 0.93, and 0.92, respectively. The test results show the SPR-Mask method has a good performance. The numbers and areas of pores, spots, umbrae and penumbrae for N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelh¨ohe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show they have similar trends, high correlations and similar N-S asymmetries. All data of YNAO are public shared at https://github.com/yzs64/YNAO sd/, which are abundant complementary to the other sunspot catalogues in the world.