A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis
A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis
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DOI：
10.3390/s19092023
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发表时间：
2019-05-01
期刊：
影响因子：
3.9
通讯作者：
Kim, Jae Ho
中科院分区：
文献类型：
--
作者：
Liu, Guoxu;Mao, Shuyi;Kim, Jae Ho
提出了一种规则彩色图像中番茄的自动检测算法，以减少光照和遮挡对番茄检测的影响。在该方法中，HOG描述子被用来训练支持向量机（SVM）分类器。提出了一种由粗到细的扫描方法来检测番茄，然后提出了一种伪彩色去除（FCR）方法来去除假阳性检测。使用非最大抑制（NMS）合并重叠结果。与其他方法相比，该算法在番茄检测中表现出明显的改善。实验结果表明，该方法的召回率、准确率和F-1值分别为90.00%、94.41%和92.15%。
An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F-1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.