Examination of bottle caps guided with image processing and deep neural networks

In recent times, several fields that exist in engineering have been heavily dominated through the integration of artificial intelligence, however machine vision systems are simultaneously being studied and observed, they encapsulate certain tools that help in deriving technical information such as the working, errors and relative faults. Such systems play a vital role in manufacturing production lines, where important factors such as efficiency and productivity are sought to be at optimum levels. This paper captures the essence of using convolutional neural networks and also the addition of VGG-19 that are used in the detection and inspection of the various type of bottle caps. The respective bottle cap classes that have been pre-determined include normal cap, no cap a misplaced cap. The implementation of this system should enable us to determine whether a bottle with a specific cap class should be rejected or accepted. High metrics of precision and accuracy of the system should be observed and beneficial to the existing and upcoming processes.