Abstract
An early identification of insects in grains is of paramount importance to avoid losses. Instead of sampling and visual/laboratory analysis of grains, we propose carrying out the insect identification task automatically, using endoscopic video analysis methods. As the classification process of moving objects in video relies heavily on precise segmentation of moving objects, we propose a new background subtraction method and comparing their results with the main methods of the literature according to a comprehensive review. The background subtraction method relies on a binarization process that uses two thresholds: a global and a local threshold. The binarized results are combined by adding details of the object obtained by the local threshold in the result to the global threshold. Experimental results performed through visual analysis of the segmentation results and using a SVM classifier suggest that the proposed segmentation method produces more accurate results than the state-of-art background subtraction methods.
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The authors thank the Brazilian agencies CNPq, CAPES, FAPEG and FAPEMIG for the financial support.
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de Geus, A.R., Batista, M.A., Rabelo, M.N., Barcelos, C.Z., da Silva, S.F. (2019). Maize Insects Classification Through Endoscopic Video Analysis. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_20
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