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Unsupervised anomaly detection and localization methods only use anomaly-free images to train the network. Ultimately, the network should be able to detect whether the input image contains anomalies and to locate the anomalous areas. There has been a lot of related research. Most of the existing research still stays in the stage of training separate models for each category. However, in industrial applications, such task setting is costly and time-consuming. Our work is to study anomaly detection methods in the continual learning setting. In this work, we use the reverse teacher-student (T-S) distillation model as the backbone network to detect anomalies in samples. To make the model able to learn in a sequence of tasks, we perform pooling distillation on the feature tensors from the student model and the embedding representations. Then, the model in the new task can retain the knowledge of the model in the previous tasks. To verify the performance of the proposed method in practical application scenarios, we also introduce a printed circuit board (PCB) defect detection dataset for continual learning tasks. This dataset divides PCB samples into multiple anomaly detection tasks based on different capturing locations, which can be used to perform validation experiments for anomaly detection algorithms based on continual learning methods. The experimental results on the MVTec AD dataset and the PCB dataset show that the detection performance of the proposed method is superior to the existing state-of-the-art (SOTA) T-S distillation anomaly detection methods in the continual learning setting. The average pixel-level AUROC (P-AUROC) reaches 0.847 on the MVTec AD dataset.

Image highlight removal is a fundamental and challenging visual task. Although fully supervised deep learning-based methods have achieved remarkable results, their performance is limited by the diversity and quantity of paired highlight images. To address this issue, we propose a student-teacher semi-supervised deep learning method based on contrastive learning for highlight removal, which integrates paired and unpaired data for boosting image highlight removal. Specifically, our semi-supervised network consists of the parallel student and teacher sub-networks with the same U-shaped CTransformer. The CTransformer integrates a dual multiscale convolution module and a parallel multiaxial self-attention module to promote local feature representation and global contextual semantic comprehension of the network. The dual multiscale convolution module realizes the representation of multiple perceptual fields by the internal and external multiscales. The parallel multiaxial self-attention module implements multidimensional autocorrelation attention with the selective fusion mechanism. Quantitative and qualitative results show that our method takes SOAT results on different datasets.

Automatic defect inspection is an important application for the development of smart factories in the era of Industry 4.0. It gathers data from production lines to train a model to automatically recognize certain types of defects. However, the defect types may vary in the production process, and it is difficult for the old model to adapt to new types of defects directly. Considering this problem, we propose an industrial defect classification framework based on lifelong learning, which continuously updates the defect classification model to adapt to different industrial scenarios as new defect appears. Specifically, a novel recursive gradient optimization (RGO) lifelong learning method is used to train the defect classification model, which only needs a fixed network capacity and does not need data replay. The proposed framework is evaluated on an experimental setup of six defect classification tasks. Extensive experiments in real scenarios are performed, demonstrating that the proposed framework can effectively relieve the catastrophic forgetting problem in lifelong learning compared with other state-of-the-art methods.

Deoxidation via chemical etch is an important step in the pretreatment of aluminium alloy chemical oxidation. Typically, the chemical deoxidation of aluminium alloy is carried out in triacid solution that contains chromic anhydride. While the use of Cr (VI) in triacid solution can cause severe chemical contamination. In this paper, we have developed a Cr-free solution for deoxidation, iron sulphate and etch inhibitors are selected to replace Cr (VI). The formula of deoxidation solution was optimised through the combination of single-factor method and Box-Behnken method. The results show that the Cr-free deoxidation solution we proposed shows a deoxidation effect comparable to that of a triacid solution.