Infrared (IR) small-target detection has been a widely studied task in IR search and tracking systems. It remains a challenging problem, especially in heterogeneous scenarios, where it is very difficult to discriminate true targets from sparse residuals in the background. A novel edge and corner awareness-based spatial–temporal tensor (ECA-STT) model is presented in this article. First, we construct an STT based on a spatial–temporal correlation analysis of the IR video background. Then, we propose an indicator to highlight the target through adjustable importance measurements of the edge and corner. The tensor-based nonlocal total variation is also adopted to describe the edges in the background. The target–background separation problem is modeled as a tensor robust principal component analysis (TRPCA) problem with the tensor rank function replaced by the tensor truncated nuclear norm. The proposed model is solved by an effective optimization algorithm derived from the alternating direction method of multipliers (ADMM). Extensive experiments verify the superior abilities of the proposed model in target enhancement and background suppression.

Infrared (IR) small target detection under complex environments is an essential part of infrared search and track systems. However, previously proposed infrared small target detection algorithms cannot achieve complete suppression of complex and significant backgrounds. The spatial-temporal information of image sequences is not fully exploited. In this paper, we present a sparse regularization-based twist tensor model for infrared small target detection. First, the twist tensor model is built via perspective conversion based on the target’s local continuity in the spatial-temporal domain, which makes the original complicated background components more structured and increases the difference between background and target. Then, the structured sparsity inducing norm is introduced to define the locality and continuity of the target. To further minimize the sparse background structures and global noise, the structured sparsity inducing norm and the l ₁ norm are combined as the target’s parse constraint. Experimental results on real scenes reveal that the suggested method can process images with high detection accuracy and outstanding background suppression ability compared to various state-of-the-art methods.

In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defective micro-capacitors, which leads to issues such as reduced detection accuracy and increased false-negative rates in existing inspection methods. To address these challenges, this paper proposes an innovative approach employing an enhanced ‘you only look once’ version 8 (YOLOv8) architecture specifically tailored for the intricate task of micro-capacitor defect inspection. The merging of the bidirectional feature pyramid network (BiFPN) architecture and the simplified attention module (SimAM), which greatly improves the model’s capacity to recognize fine features and feature representation, is at the heart of this methodology. Furthermore, the model’s capacity for generalization was significantly improved by the addition of the weighted intersection over union (WISE-IOU) loss function. A micro-capacitor surface defect (MCSD) dataset comprising 1358 images representing four distinct types of micro-capacitor defects was constructed. The experimental results showed that our approach achieved 95.8% effectiveness in the mean average precision (mAP) at a threshold of 0.5. This indicates a notable 9.5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors.

Pan-sharpening, a task involving information fusion, entails merging panchromatic (PAN) images with high spatial resolution and low-resolution multispectral (LRMS) images in order to obtain high-resolution multispectral (HRMS) images. Due to deep learning’s excellent regression capabilities, it has recently become the dominating technique for this assignment. Meanwhile, the development of the transformer, a novel deep learning architecture for natural language processing, has provided researchers with new insights. In this letter, we seek to extend transformer’s excellent mechanisms to pixel-level fusion challenges. We designed a parallel convolutional neural network structure for learning both the regions of interest from the LRMS images and the residuals required for regression to HRMS images. Then, in our proposed pixelwise attention constraint (PAC) module, the residuals will be changed utilizing the learned region of interest. In addition, we presented a novel multireceptive-field attention block (MRFAB) to frame our network. Experiments on two datasets also show that our work is better than the mainstream algorithms at both indicators and visualization.

Infrared small target detection plays an important role in the infrared search and track (IRST) system. However, the infrared small target images often suffer from low contrast. In this paper, we propose an infrared small target detection method that improves the target contrast and suppresses background clutters based on spatial-temporal enhancement using the quaternion discrete cosine transform (QDCT). The proposed method is twofold: 1) we propose to detect the infrared small target by constructing the quaternion feature map for infrared images. The quaternion integrates four feature maps, including the kurtosis feature, two directional feature maps extracted by steerable filtering in spatial domain and motion feature in the temporal domain. 2) Then the quaternion is input into QDCT, and the saliency maps of each feature channel can be obtained by sign function processing. The final detection result is obtained by inverse QDCT to the quaternion. Compared to several state-of-the-art algorithms, the proposed method has a lower false alarm rate when the same positive detection rate is achieved.

RGBT object tracking is an important research topic due to the complementary features of visible and thermal infrared images. However, the established fast-tracking Siamese series RGBT trackers do not fully exploit the predicted results and global information, which leads to poor tracking performance in challenging scenarios such as target object deformation and fast movement. To address these challenges, we propose a dynamic feature-memory transformer RGBT tracking framework in this paper. Precisely, we extract the features of visible and infrared image pairs separately using convolutional networks. We then fuse the complementary semantic information using our proposed complementary semantic fusion module, which consists of a group convolution, a channel attention structure, a flattening operation, and channel reduction. Next, we concatenate the dynamic memory feature extracted from the predicted frame pair, the template pair feature, and the search region pair feature. Then input concatenated features into transformer encoder-decoder modules to acquire the global information and further fuse the RGBT features. Finally, we predict the object state using the tracking head while deciding whether to update the dynamic memory feature using the reliability estimator. We conducted extensive experiments on the widely used RGBT234 and LasHeR datasets to evaluate the proposed framework’s performance. The experimental results show that our method outperforms state-of-the-art RGBT trackers, demonstrating the effectiveness of our approach. Overall, our proposed framework leverages visible and infrared images and incorporates global information and dynamic memory features, offering a promising solution to the challenges in RGBT object tracking. Code will be open sourced at https://github.com/ELOESZHANG/DFMTNet.