Computer-vision as an ErrorRecognition Tool for IndustryProduction Lines : A comparative study of models and neural networks

Sammanfattning: To use machine learning as a tool to help humans perform tasks is a common occurrence these days. Machine learning integration in the wood processing industry however, is more rare. Therefore it is suitable to research if machine learning works for some problem in this area. After all, it is unknown which methods are the most suitable for this task. The problem is to recognize when a wooden plank is somehow erroneous within a 30 seconds video clip from a production line. Eight approaches were initially chosen and from these, four was ultimately selected to be compared. The approaches that will be used to investigate the problem are ZFNet, DenseNet, Convolutional autoencoder with SVM, and YOLO. These will be compared head to head, to find which method overall fits this problem best. All methods show good results according to the metrics. What is discovered is that ZFNet is the most likely the best method for this scenario. This is because even though DenseNet and YOLO has a perfect F1 score, the likelihood of overfitting is high based on the graphs and in combination with that, ZFNet has the fastest prediction time, thus ZFNet is concluded to be the best method. From here future work can build upon which methods fits this scenario, for example in a multiclass classification problem which is the immediate follow up. Since ZFNet is a simple model as the classes increase there might be a need to switch to DenseNet to match the complexity better.