Food defect detection is crucial for the automation of food production and processing. Potato surface remains challenging due to irregular shape potato individuals various types defects. This paper employs deep convolutional neural network (DCNN) models detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, ...

Abstract As one of the very common industrial products, strip steel plays a important role in various fields. Steel production has received extensive attention. In production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, their presence direct impact on product’s quality. Therefore, it is great significance develop efficient accurate surfa...

As the operational time of railway increases, rail surfaces undergo irreversible defects. Once defects occur, it is easy for them to develop rapidly, which seriously threatens safe operation trains. Therefore, accurate and rapid detection surface very important. However, in defects, there are problems, such as low contrast between background, large scale differences, insufficient training sampl...

Surface defect identification is an essential task in the industrial quality control process, which visual checks are conducted on a manufactured product to ensure that it meets standards. Convolutional Neural Network (CNN) based surface method has proven outperform traditional image processing techniques. However, real-world datasets limited size due expensive data generation process and rare ...