ERROR DETECTION IN PRODUCTION LINES VIA DEPENDABLE ARCHITECTURES IN CONVOLUTIONAL NEURAL NETWORKS

Sammanfattning: The need for products has increased during the last few years, this high demand needs to bemet with higher means of production. The use of neural networks can be the key to increasedproduction without having to compromise product quality or human workers well being. This thesislooks into the concept of reliable architectures in convolutional neural networks and how they canbe implemented. The neural networks are trained to recognize the features in images to identifycertain objects, these recognition is then compared to other models to see which of them had the bestprediction. Using multiple models creates a reliable architecture from which results can be produced,these results can then be used in combinations with algorithms to improve prediction certainty. Theaim of implementing the networks with these algorithms are to improve the results without havingto change the networks configurations.