@article{FerFraLam23-IJIS-IJ,
author = {Ferrari, Niccol{\`o}
and Fraccaroli, Michele
and Lamma, Evelina},
title = {GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module},
journal = {International Journal of Intelligent Systems},
year = {2023},
month = {Sep},
day = {02},
publisher = {Hindawi},
volume = {2023},
pages = {7773481},
abstract = {Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task that is usually achieved using a basic comparison between generated image and the original one, implementing some blob analysis or image-editing algorithms in the postprocessing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a generative adversarial network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using preprocessing algorithms, formerly developed with blob analysis and image-editing procedures. To test our model, we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network.},
issn = {0884-8173},
doi = {10.1155/2023/7773481},
url = {https://doi.org/10.1155/2023/7773481}
}
@article{FraCasBizLam23-AI-IJ,
abstract = {Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML) in this field relies on the ability of training a network to learn to inspect images to determine the presence or not of anomalies. Frequently, in Industry 4.0 w.r.t. the anomaly detection task, the images to be analyzed are not optimal, since they contain edges or areas, that are not of interest which could lead the network astray. Thus, this study aims at identifying a systematic way to train a neural network to make it able to focus only on the area of interest. The study is based on the definition of a loss to be applied in the training phase of the network that, using masks, gives higher weight to the anomalies identified within the area of interest. The idea is to add an Overlap Coefficient to the standard cross-entropy. In this way, the more the identified anomaly is outside the Area of Interest (AOI) the greater is the loss. We call the resulting loss Cross-Entropy Overlap Distance (CEOD). The advantage of adding the masks in the training phase is that the network is forced to learn and recognize defects only in the area circumscribed by the mask. The added benefit is that, during inference, these masks will no longer be needed. Therefore, there is no difference, in terms of execution times, between a standard Convolutional Neural Network (CNN) and a network trained with this loss. In some applications, the masks themselves are determined at run-time through a trained segmentation network, as we have done for instance in the "Machine learning for visual inspection and quality control" project, funded by the MISE Competence Center Bi-REX.},
author = {Fraccaroli, Michele and Bizzarri, Alice and Casellati, Paolo and Lamma, Evelina},
date = {2023/12/12},
date-added = {2023-12-18 20:50:22 +0100},
date-modified = {2023-12-18 20:50:22 +0100},
doi = {10.1007/s10489-023-05177-0},
id = {Fraccaroli2023},
isbn = {1573-7497},
journal = {Applied Intelligence},
title = {Exploiting CNN's visual explanations to drive anomaly detection},
url = {https://doi.org/10.1007/s10489-023-05177-0},
year = {2023},
bdsk-url-1 = {https://doi.org/10.1007/s10489-023-05177-0}
}
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