On line detection of defective apples using computer vision system combined with deep learning methods. (December 2020) - Fan, Shuxiang; Li, Jiangbo; Zhang, Yunhe; Tian, Xi; Wang, Qingyan; He, Xin; Zhang, Chi; Huang, Wenqian
Abstract: 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. Highlights: A CNN model was proposed for inspection of defective apples. The CNN model was more promising than traditional SVM classification method. Effective on-line sorting of apples by applying the CNN model.