Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists in videos, which makes it difficult to accurately perceive anomalous activities. In this paper, we exploit the idea of a training model based on the “Cloze Test” strategy in natural language processing (NLP) and introduce a novel unsupervised learning framework to encode both motion and appearance information at an object level. Specifically, to store the normal modes of video activity reconstructions, we first design an optical stream memory network with skip connections. Secondly, we build a space–time cube (STC) for use as the basic processing unit of the model and erase a patch in the STC to form the frame to be reconstructed. This enables a so-called ”incomplete event (IE)” to be completed. On this basis, a conditional autoencoder is utilized to capture the high correspondence between optical flow and STC. The model predicts erased patches in IEs based on the context of the front and back frames. Finally, we employ a generating adversarial network (GAN)-based training method to improve the performance of VAD. By distinguishing the predicted erased optical flow and erased video frame, the anomaly detection results are shown to be more reliable with our proposed method which can help reconstruct the original video in IE. Comparative experiments conducted on the benchmark UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets demonstrate AUROC scores reaching 97.7%, 89.7%, and 75.8%, respectively.

It is inevitable that defects happen to key components of the long-running high-speed trains. Thus as an effective inspection approach for defects, image detection becomes significantly important for operation and maintenance in the railway industry. However, a massive number of images collected by inspection devices challenge traditional methods based on manual effort. To address this issue, this paper proposed an automatic detection method, termed as multi-stage pipeline for defect detection (MPDD). MPDD includes two stages, component detection stage improves RPN anchor mechanism and way of feature fusion to promote detection performance, defect classification stage combines super-resolution strategy with CNN to improve defect classification performance. Experiments on high-speed train defect dataset shown that MPDD can reach the highest mAP of 0.792. The mAP on NEU surface defect database reached to 0.765 at the speed of 203ms per image.

The existing object detection methods are facing significant challenges when applied to marine environments, such as underwater image degradation caused by absorption and scattering of light, and domain transfer in water bodies with different water qualities. In this letter, we propose a marine organism detection framework to improve the detection performance and the domain generalization performance. First, a double domains data augmentation is proposed. This method combines underwater image enhancement and water quality transfer to improve the domain diversity of the original dataset. Second, we utilize the self-attention operations and the convolution to improve the detection performance of the YOLOv7, fully utilizing the advantages of self-attention and convolutional computation. Meanwhile, this model uses SIoU loss to accelerate convergence speed and improve the regression. Experiments on the URPC2019 and URPC2020 datasets show that the proposed object detection method achieves a mean average precision of 82.3% and 83.6%, respectively, which is superior to all other methods used for comparison.