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Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Computers, Materials & Continua 2021年第5期67卷 2547-2568页

作者： Kangjik kim hyunbin kim Junchul Chun Mingoo Kang Min Hong Byungseok Min Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Department of IT Contents Hanshin UniversityOsan-si18101Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan-si31538Korea R&D Center XAVIS Co.LtdSeongnam-si13202Korea

Physical contamination of food occurs when it comes into contact with foreign *** objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or ***,a preventive method that can detect and remove foreign objects in advance is *** studies have attempted to detect defective products using deep learning *** it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning *** paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning *** this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste *** on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test *** evaluated this method on images of pasta,snacks,pistachios,and red beans under the same *** results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged *** detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly *** addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

关键词： Deep-learning anomaly detection packaged food X-ray detection foreign substances detection abnormal data augmentation

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