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Defect Detection of Metal Nuts Applying Convolutional Neural Networks

Since the human inspection of small metal parts is complex, time-consuming and prone to human error, a convolutional neural network for the detection of defects on metal nuts was developed in order to grant fast and robust quality controls. For this approach, we built an image classification algorithm based on the Xception architecture. The evaluation of the trained model is robust and achieves reliable results after applying a hold-out 5-fold cross-validation. Implementing this algorithm on the MVTec Anomaly Detection dataset outperforms the existing benchmark on defect detection for metal nuts with a balanced accuracy of 90.00% and a value of 0.99 for the area under the curve.