Abstract:The foreign matter detection in bottled mineral water was studied, in order to find out the foreign matter and impurities in mineral water, which has important applications in the mineral water manufacturing industry. However, the traditional machine has a low vision detection accuracy, large background interference, and high rates of missed detection and false detection. In order to solve the above problems, a small-scale feature extraction algorithm for foreign matter detection in bottled mineral water was proposed. The algorithm mainly included a discriminant feature learning module, data augmentation module, and fine-grained information acquisition module. For mineral water foreign matter, in the discriminant feature learning module, the clustering method was used to design a reasonable a prior frame size and process the feature map designing prior box size by clustering and processing the feature map. The model was given discriminant features for extraction through imposing loss constraint on the output of the network. The increase in the number of samples can have a positive effect on the improvement of the performance of detection algorithm. To achieve this, a data augmentation module. In this module, the self-built data set is expanded by random channel shuffling and reorganization. Furthermore, in the fine-grained information acquisition module, a small-scale feature learning mechanism is used to characterize foreign matter. The experimental results have proved the superiority and effectiveness of the algorithm proposed in this research. The average accuracy of foreign matter detection in bottled mineral water was 96.22%, with the mAP value as 83.84%, recall rate as 86.31%, and detection speed as 50 f/s. Therefore, this study can provide reliable technical support for detecting foreign matter in bottled mineral water.
