Classificação de insetos em milho à granel por meio de análise de vídeos endoscópicos

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Universidade Federal de Goiás


Insects cause significant losses of stored grains in both quantity and quality. In the scenary, it is of paramount importance an early identification of insects in grains to take control measures. Instead of sampling and visual/laboratory analysis of grains, we propose to carry out the insects identification task automatically, using computational methods to perform endoscopic video analysis. The videos are recorded inside of grains warehouses by an endoscopic camera. As the classification process of moving objects in video rely heavily on precise segmentation of moving objets, we propose a new method of background subtraction and compared their results with the main methods of the literature according to a recent review. The main innovation of the background subtractionmethod rely on the 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 of 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.



Classificação de insetos, Subtração de plano de fundo, Extração de características, Classificador SVM, Insects classification, Background subtraction, Feature extraction, SVM classifier


GEUS, A. R. Classificação de insetos em milho à granel por meio de análise de vídeos endoscópicos. 2016. 61 f. Dissertação (Mestrado em Modelagem e Otimização) - Universidade Federal de Goiás, Catalão, 2016.