Abstract
Insects are abundant species on the earth, and the task of identification and identification of insects is complex and arduous. How to apply artificial intelligence technology and digital image processing methods to automatic identification of insect species is a hot issue in current research. In this paper, the problem of automatic detection and classification of butterfly photographs is studied, and a method of bio-labeling suitable for butterfly classification is proposed. On the basis of YOLO algorithm [1], by synthesizing the results of YOLO models with different training mechanisms, a butterfly automatic detection and classification algorithm based on YOLO algorithm is proposed. It greatly improves the generalization ability of Yolo algorithm and makes it have better ability to solve small sample problems. The experimental results show that the proposed annotation method and integrated YOLO algorithm have high accuracy and classification rate in butterfly automatic detection and classification.
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Acknowledgement
Thank you here for the organizers of Baidu Encyclopedia Butterfly related pages, collectors of university open biological data sets and collectors of data provided by China Data Mining Conference. They have provided valuable data for our experiments.
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Liang, B., Wu, S., Xu, K., Hao, J. (2020). Butterfly Detection and Classification Based on Integrated YOLO Algorithm. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_55
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DOI: https://doi.org/10.1007/978-981-15-3308-2_55
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