On line detection of defective apples using computer vision system combined with deep learning methods

文献类型： 外文期刊

第一作者： Fan, Shuxiang

作者： Fan, Shuxiang;Li, Jiangbo;Zhang, Yunhe;Tian, Xi;Wang, Qingyan;He, Xin;Zhang, Chi;Huang, Wenqian;Fan, Shuxiang;Li, Jiangbo;Zhang, Yunhe;Tian, Xi;Wang, Qingyan;He, Xin;Zhang, Chi;Huang, Wenqian;Fan, Shuxiang;Li, Jiangbo;Zhang, Yunhe;Wang, Qingyan;Zhang, Chi;Huang, Wenqian;Fan, Shuxiang;Li, Jiangbo;Zhang, Yunhe;Wang, Qingyan;Zhang, Chi;Huang, Wenqian

作者机构：

关键词： Apple; Defects; Convolutional neural network; SVM; Deep learning

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.354； 五年影响因子：5.144 ）

ISSN： 0260-8774

年卷期： 2020 年 286 卷

页码：

收录情况： SCI

摘要： A deep-learning architecture based on Convolutional Neural Networks (CNN) and a cost-effective computer vision module were used to detect defective apples on a four-line fruit sorting machine at a speed of 5 fruits/s. A CNN based classification architecture was trained and tested, with the accuracy, recall, and specificity of 96.5%, 100.0%, and 92.9%, respectively, for the testing set. An inferior performance was obtained by a traditional image processing method based on candidate defective regions counting and a support vector machine (SVM) classifier, with the accuracy, recall, and specificity of 87.1%, 90.9%, and 83.3%, respectively. The CNN-based model was loaded into the custom software to validate its performance using independent 200 apples, obtaining an accuracy of 92% with a processing time below 72 ms for six images of an apple fruit. The overall results indicated that the proposed CNN-based classification model had great potential to be implemented in commercial packing line.

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