Convolutional neural networks for recognition and segmentation of aluminium profiles

Learning to automatically recognize objects in the real world is a very important and stimulating challenge. This work deals with the problem of detecting aluminium profiles within images, using hierarchical representations such as those based on deep learning methods. The use of regional CNN, a conceptually simple, flexible, and general framework for object instance segmentation, allows to exceed the previous state-of-the-art results. This approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Neural network training uses ResNet networks of depth 50 or 101 layers. In particular, the training dataset consists of synthetic data generated by CAD files. The Dataset creation process is fundamental: experimental results show that trivial datasets lead to poor detection performance. A rich dataset, instead, including more complex images, allows the network to learn more and better guaranteeing excellent results. How to get more data, if you do not have more data? To get more data, we just need to make minor alterations such as flips, scale or rotations to existing dataset. This process is known as Data augmentation. The performance of the proposed system strongly depends on the dataset used for training and on the backbone architecture used. This why, adopting a strategy that generates a priori a very large dataset the time required to create the annotation file grows almost exponentially. The validation and test dataset, on the other hand, consists of real images captured by cameras. The distinctiveness of this work consists to train a deep neural network with synthetic data (aluminium CAD files) and verify if it on real data. Experiments show that the implementation of architecture, as described above, leads to good performance in automatic detection and classification. Future work will be addressed to improve the network training process together with the architecture, the algorithms and the dataset creation process. The latter is proved to be fundamental for the balance and optimization of the whole process. The way is to develop not much augmented datasets focusing on online data augmentation during network training.