Anomaly detection is an essential task in industrial applications. Traditional anomaly detection algorithms based on convolutional neural networks (CNNs) struggle to extract global context information, resulting in poor anomaly detection performance. Moreover, the diversity and inherent uncertainty of anomalous samples present considerable constraints on the effectiveness of traditional anomaly detection algorithms. In this paper, a novel Swin Transformer Unet with Random Masks for self-supervised anomaly detection is proposed. First of all, a random mask strategy(RMS)is adopted to generate simulated anomalies in anomaly-free samples to solve the limited availability of abnormal samples during the training phase. Furthermore, to enhance the overall feature representation for anomaly detection tasks, the Swin Transformer Unet is utilized as the backbone network to extract local features and global contextual information from multi-scale feature maps. Experimental results on the industrial dataset MVTec AD demonstrate that our model achieves comparable performance in terms of anomaly detection.
In recent studies, remote sensing object detection methods based on deep learning have emerged as a primary concern in environmental monitoring, military investigation, and hazard response. However, many difficulties, such as complex backgrounds, dense target quantities, large-scale variations, and non-uniform distribution, lead to many parameters and complex network structures, thus limiting the accuracy of the detector and slowing the inference speed. To address these issues, we propose a lightweight and efficient object detector for remote sensing images. First, an asymmetric convolution with the visual attention mechanism is reconstructed to decrease the complexity and strengthen the feature representation ability. Then, an adaptive feature selection structure is designed to extract discriminative feature information, which can adaptively model the shapes of objects by introducing deformable convolution to obtain a stronger geometric feature representation. To reduce information loss across different channels and spatial locations, a hybrid receptive field module is also proposed to increase the receptive field model by mixing dilated convolutional layers with different dilation rates. Finally, experimental results on the DIOR dataset show that our approach significantly improves detection accuracy and running speed.
Infrared small target detection (IRSTD) plays an essential role in many fields such as air guidance, tracking, and surveillance. However, due to the tiny sizes of infrared small targets, which are easily confused with background noises and lack clear contours and texture information, how to learn more discriminative small target features while suppressing background noises is still a challenging task. In this paper, a context-aware cross-level attention fusion network for IRSTD is proposed. Specifically, a self-attention-induced global context-aware module obtains multilevel attention feature maps with robust positional relationship modeling. The high-level feature maps with abundant semantic information are then passed through a multiscale feature refinement module to restore the target details and highlight salient features. Feature maps at all levels are fed into a channel and spatial filtering module to compress redundant information and remove background noises, which are then used for cross-level feature fusion. Furthermore, to overcome the lack of publicly available datasets, a large-scale multiscene infrared small target dataset with high-quality annotations is constructed. Finally, extensive experiments on both public and our self-developed datasets demonstrate the effectiveness of the proposed method and the superiority compared with other state-of-the-art approaches.
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