Abstract
Multi-Stream Deep Convolutional Neural Network for PET Preform Surface Defects Detection and Classification
Highlights
Due to the growing demand of the production and consumption for cosmetic bottles, beverage and mineral water bottles, medical plastic packaging and edible oil bottles formed by blow molding the preform, PET preform with the advantages of non-toxic, uniform quality distribution and good insulation has been widely used in the field of daily chemical products packaging
A multi-stream deep CNN model taking VGG16 and GoogLeNet as the backbone networks is studied for the surface defects detection and classification of PET preform in this paper, which improves the structure of the deep CNN and fuses multiple features to obtain better detection accuracy
The contributions of the surface defects detection and classification for PET preform based on multi-stream deep CNN model can be summarized as follows: (1) Through the multi-stream network model, a multiscale feature fusion mechanism is realized by combining the high-level semantic features with intermediate contour features of deep CNN
Summary
Due to the growing demand of the production and consumption for cosmetic bottles, beverage and mineral water bottles, medical plastic packaging and edible oil bottles formed by blow molding the preform, PET preform with the advantages of non-toxic, uniform quality distribution and good insulation has been widely used in the field of daily chemical products packaging. There are few multi-stream deep neural network methods which integrate shallow and deep features on the aspect of defect detection and classification for PET preform. A multi-stream deep CNN model taking VGG16 and GoogLeNet as the backbone networks is studied for the surface defects detection and classification of PET preform in this paper, which improves the structure of the deep CNN and fuses multiple features to obtain better detection accuracy. The contributions of the surface defects detection and classification for PET preform based on multi-stream deep CNN model can be summarized as follows:. CHEN Hong-cai et al [7] proposed a deep CNN detection model in order to detect the appearance defects of medical glass bottles accurately and quickly, and the multi-scale feature information of glass bottles was extracted by connecting and standardizing the shallow and deep feature vectors of the neural network structure. According to the above analyses, the multi-stream deep neural network structure has many effects on image detection and classification
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