Vision Based Crack Detection using Transfer Learning in Metal Forming Processes
Crack detection is an important quality control task for metal working processes. In small and medium size businesses quality inspection is often performed manually. This approach is time consuming and sensitive to errors. In this work an automated visual inspection system for defect detection is presented which classifies cracks in quality control images of a metal forming process taken under varying ambient conditions. The classification is based on transfer learning with AlexNet and GoogLeNet. Supervised learning based on a stochastic gradient descent (sgd) solver was used to train the convolutional neural network. Oversampling and data augmentation with rotated, scaled and shifted images were applied to overcome limitations of the available dataset like huge imbalances of training data sizes for different classes and to prevent overfitting, respectively. Different training parameters were compared and tested to obtain the best classification results for the specific task. GoogLeNet outperformed AlexNet and reached an accuracy of 0.998 and an F1 score of 0.836 for crack detection. Data pre processing and labeling as well as tuning of the training parameters had a significant influence on classification accuracy and require human decision making.
2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)