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A multispectral image based object detection approach in natural scene

机译:基于多光谱图像的自然场景中的物体检测方法

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In this paper, we investigate the feasibility of blueberry detection based on multispectral image, and build robust classification models, which tolerate outdoor illumination changes and complicated background information. In the fruit growth, there are three stages, mature, near mature, and young. Base on the multispectral image and the normalized vegetation index NDVI, we construct a classifier to detect the different growth stage of the fruits by analyzing the color component and using the C4.5 algorithm. The experimental results show that the correct recognition rate of the mature, approximate mature and immature fruit are 74.58%, 79.91% and 86.52%, respectively, which demonstrate that the proposed classifier has a better recognition effect for immature fruits.
机译:在本文中,我们研究了基于多光谱图像的蓝莓检测的可行性,并建立了容忍户外照明变化和复杂背景信息的鲁棒分类模型。 在水果生长中,有三个阶段,成熟,近成熟,而且年轻。 基于多光谱图像和归一化植被指数NDVI,我们通过分析颜色分量并使用C4.5算法来构建分类器来检测水果的不同生长阶段。 实验结果表明,成熟,近似成熟和未成熟果的正确识别率分别为74.58%,79.91%和86.52%,表明该分类器对未成熟果具有更好的识别效果。

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