Industrial Anomaly Detection with Skip Autoencoder and Deep Feature Extractor

In this paper, we introduce a one-class learning approach for detecting modifications in assembled printed circuit boards (PCBs) based on photographs taken without tight control over perspective and illumination conditions. Anomaly detection and segmentation are essential for several applications, where collecting anomalous samples for supervised training is infeasible. Given the uncontrolled environment and the huge number of possible modifications, we address the problem as a case of anomaly detection, proposing an approach that is directed towards the characteristics of that scenario, while being well suited for other similar applications. We propose a loss function that can be used to train a deep convolutional autoencoder based only on images of the unmodified board—which allows overcoming the challenge of producing a representative set of samples containing anomalies for supervised learning. We also propose a function that explores higher-level features for comparing the input image and the reconstruction produced by the autoencoder, allowing the segmentation of structures and components that differ between them. Experiments performed on a dataset built to represent real-world situations (which we made publicly available) show that our approach outperforms other state-of-the-art approaches for anomaly segmentation in the considered scenario, while producing comparable results on a more general object anomaly detection task.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image-Based Detection of Modifications in Assembled PCBs with Deep Convolutional Autoencoders

Oliveira

Nassu

Wehrmeister

2023

“…Methods for image reconstruction in anomaly detection, in contrast, include autoencoders [3][4][5][6], variational autoencoders [7][8][9], and generative adversarial networks (GANs) [10,11]. Most of them mainly input normal images and train the network to extract high-dimensional features and then reconstruct them into normal images.…”

Section: Related Workmentioning

confidence: 99%

“…For example, reconstruction models based on autoencoders [2][3][4][5][6][7][8][9] or generative adversarial networks (GANs) [10][11][12] aim to reconstruct normal images and locate anomalies based on the reconstruction error. However, due to their powerful generalization ability, abnormal regions may still remain anomalous even after reconstruction [13].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection via Progressive Reconstruction and Hierarchical Feature Fusion

Liu,

Zhu,

Feng

et al. 2023

The main challenges in reconstruction-based anomaly detection include the breakdown of the generalization gap due to improved fitting capabilities and the overfitting problem arising from simulated defects. To overcome this, we propose a new method called PRFF-AD, which utilizes progressive reconstruction and hierarchical feature fusion. It consists of a reconstructive sub-network and a discriminative sub-network. The former achieves anomaly-free reconstruction while maintaining nominal patterns, and the latter locates defects based on pre- and post-reconstruction information. Given defective samples, we find that adopting a progressive reconstruction approach leads to higher-quality reconstructions without compromising the assumption of a generalization gap. Meanwhile, to alleviate the network’s overfitting of synthetic defects and address the issue of reconstruction errors, we fuse hierarchical features as guidance for discriminating defects. Moreover, with the help of an attention mechanism, the network achieves higher classification and localization accuracy. In addition, we construct a large dataset for packaging chips, named GTanoIC, with 1750 real non-defective samples and 470 real defective samples, and we provide their pixel-level annotations. Evaluation results demonstrate that our method outperforms other reconstruction-based methods on two challenging datasets: MVTec AD and GTanoIC.

“…Tang et al proposed a skip autoencoder to improve the accuracy of anomaly detection and address labeling issues. Leveraging a pre-trained feature extractor and skip connections, the proposed method achieved better performance, showing a maximum area under the curve (AUC) of 0.98 [ 20 ]. Upadhyay et al developed a U-Net-based deep learning framework to detect engine defects.…”

Section: Introductionmentioning

confidence: 99%

Two-Stream Network One-Class Classification Model for Defect Inspections

Lee

Luo

Lee³

et al. 2023

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.