Abstract
In this paper, we propose a deep learning framework to automatically detect pollen grains instead of the manual counting of pollen numbers under an optical microscope. Specifically, we first establish a large-scale dataset of pollen grains, which contains 3000 images of five subcategories. All the images in our dataset are scanned by an optical microscope. Then, a pollen grain detector (PGD) based on deep learning is designed to eliminate the effects of noise and capture subtle features of pollen grains. Finally, extensive experiments are conducted and show that the proposed PGD method achieves the best performance (84.52% mAP).
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This study is supported by Beijing Municipal Science and Technology Project with no. Z191100009119013.
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Xiong, C., Li, J., Pei, Y., Kang, J., Jia, Y., Ye, C. (2022). An Automatic Pollen Grain Detector Using Deep Learning. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2021. Lecture Notes in Electrical Engineering, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-16-8052-6_4
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DOI: https://doi.org/10.1007/978-981-16-8052-6_4
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