DEEP LEARNING MODEL IN IMAGE SEGMENTATION APPLIED TO SOYBEAN LEAVES
Abstract
Image segmentation is a highly relevant step in image processing. This step defines regions of interest in images to facilitate object identification and pattern recognition. Traditional image segmentation methods are highly sensitive to environmental and lighting variations. Consequently, Deep Learning models offer modern image processing techniques to address these issues. Convolutional neural networks have evolved and established themselves as one of the great promises in the field of Deep Learning-based image processing. In this work, we investigate and modify the deconvolutional neural network with the objective of segmenting images of soybean leaves. This research proposes a deep learning architecture optimized for soybean leaf segmentation with low computational cost. Through an applied, quantitative methodology and an experimental setup, the proposal is evaluated and compared with traditional models and other established convolutional neural networks. Validation uses statistical metrics and noise stress tests to prove the robustness and accuracy of the proposal. The results are compared with several traditional methods and with traditional supervised machine learning for the image segmentation task. Performance was evaluated using the Dice, Recall, and Specificity metrics. The proposed approach achieved promising accuracy values above 95% in all test datasets, even with the insertion of alterations to the images.
Author Biographies
PhD in Electrical Engineering and Master’s in Computer Science, with a degree in Computer Engineering. Associate Professor at the Universidade do Estado de Mato Grosso.
PhD and Master’s in Computer Science, with a degree in Computer Science Teaching. Associate Professor at the Universidade do Estado de Mato Grosso.
PhD and Master’s in Computer Science, with a degree in Computer Science Teaching. Associate Professor at the Universidade do Estado de Mato Grosso.
PhD in Electrical Engineering and Master’s in Physics, with a degree in Physics. Associate Professor at the Universidade do Estado de Mato Grosso.
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