This work is concerned with the asynchronous output feedback sliding mode control (SMC) of stochastic nonlinear Markov jump systems (MJSs) via Takagi–Sugeno fuzzy models. Due to some real-world environment limitations, the actual system modes that are not directly available for controller synthesis are known as hidden modes. Then the sliding surface/sliding mode controller modes are featured as observable modes, and the relationship between these two concepts is established by employing emission probabilities. As a two-layer stochastic process, the hidden Markov model (HMM) governs the jump parameters and characterizes the asynchronous mode switching phenomenon between the original plant and the sliding surface/sliding mode controller. By integrating the sliding surface with the dynamical features of fuzzy MJSs, the dynamics of the sliding motion are described by constructing a T–S fuzzy singular MJS. Under a unified convexification setup, novel dissipative performance and stochastic stability analysis results on the sliding motion are proposed. In view of the full MJS states also not measurable, a novel observed-mode-based asynchronous output feedback dynamic SMC synthesis approach is propounded to ensure the MJSs’ states are located in a vicinity of the sliding surface. Illustrative simulation examples are finally provided to validate the superiority and effectiveness of the developed scheme.

Developing reliable, generalized, and accurate defect detection technology for photovoltaic (PV) manufacturers is particularly critical with the demand for production line expansion. Existing technologies perform well when handling independently and identically distributed (IID) data. However, their performance significantly reduces when they encounter domain shift problems, including style and instance bias, prompted by production line expansion. In this paper, we propose a novel Dual Bias Adjustment Network (DBAN) to enhance the generalization and reliability of PV defect detection. Specifically, we construct a Global Style-Generalized Contrastive Learning (GSCL), which uses nonlinear transformation functions and contrastive learning strategies to enhance model adaptability for different style changes and global discriminative ability, effectively overcoming the style bias problem. We design a Test-Time Prototype Adjustment (TTPA) that employs graph methods and prototype learning to adjust feature representations accurately. TTPA enhances prediction reliability during testing via dynamic prototype repositories and memory mechanisms, effectively addressing instance deviations. We conduct comprehensive experiments proving that DBAN achieves optimal performance, surpassing other advanced algorithms. Moreover, GSCL and TTPA add little inference time to the model, making them suitable for practical industrial applications. Finally, we conduct extensive experiments on the public domain-shifted PV dataset ELES, where our model achieves state-of-the-art performance in Single-domain generalized object detection. Note to Practitioners—This work proposes a practical defect detection solution, DBAN, enabling PV manufacturers to maintain reliable quality control across multiple production lines. The model can be directly integrated into existing inspection systems by deploying DBAN software on a central processing server that receives EL images from production lines. Trained with historical defect data from one production line, the model can automatically inspect products from expanded lines and continuously update its detection capabilities using real-time test data without retraining. DBAN enables practitioners to monitor multiple lines through a unified interface while maintaining high-quality standards, thus reducing equipment and labor costs. The system continuously enhances detection accuracy under varying manufacturing conditions, making it especially valuable for manufacturers seeking efficient, scalable production while ensuring product quality.

The Text-to-Image Person Re-identification (TI-ReID) task objective is to precisely identify the person’s images with the textual description of the person. The mainstream research methods focus on cross-modal aligning local features, and overlook the learning of intra-modal and cross-modal relationships between different features. This renders the person features lacking in high-level semantic information. To resolve such issues, we propose the Progressive Relationship-Mining Graph Network (RMGNet), including the Intra-Modal Relationship-Mining (IMRM) and the Cross-Modal Relationship-Mining (CMRM) module. These modules are employed to model and mine semantic relationship information among different features. Specifically, the IMRM module models and mines the high-level semantic interrelationships inherent in the image and text features. The CMRM module introduces the nearest neighbor method to model cross-modal semantic relationships to enhance the cross-modal semantic correspondence capabilities of person features. On this basis, we design the Adaptive Corner Center (Acc) loss and the Coarse-to-Fine Learning (C2FL) strategy. These ensure the network receives consistent and effective metric learning supervision throughout the entirety of the training process. To validate the efficacy of the proposed method, extensive experiments are conducted on three prevalent datasets: CHUK-PEDES, ICFC-PEDES, and RSTPReid. The achieved mAP of 70.59%, 41.62%, and 49.58% surpassed those current state-of-the-art methods.

The photovoltaic industry is a strategic industry with international competitive advantages and is developing towards a larger scale, higher efficiency, and higher quality. However, current researchers have not built a pixel-level defect inspection system for large-scale photovoltaic production processes. This paper proposes an intelligent defect segmentation system combining the Internet of Things (IoT), artificial intelligence (AI), and edge computing for quality inspection of large-scale photovoltaic production lines. The intelligent factory based on this system is highly intelligent and deeply integrated, which can significantly reduce labor costs and improve factory productivity and product quality. The system’s core uses edge computing to segment cells in real-time by a lightweight defect segmentation model. Specifically, this paper proposes a lightweight yet effective architecture named Low-cost Defect Segmentation Network (LDSN). An Efficient Split (ES) block is designed to support more channels and improve model accuracy without adding much computational complexity. Moreover, the ES block can express multiscale features in a finer granularity and enhance the information interaction between grouping features. In the decoding structure, a Dual Focus Attention (DFA) that efficiently captures long-range spatial and channel information is proposed. Comprehensive experiments have been performed on a low-end PC with an NVIDIA GeForce RTX3060 GPU and an Intel Core i5-10600KF. LDSN-T-Lite achieves 84FPS and the F-measure OIS of 0.827, which only has 166K parameters and 395.6M memory usage on our PSCDE1 dataset. A bigger version of LDSN-B achieves the F-measure OIS of 0.872, significantly outperforming current methods.

High-performance defect segmentation techniques are essential for the high-quality manufacturing of polycrystalline solar cells. Edge detection is an effective technique to accurately locate the edge of defects. However, the existing methods ignore global channel information and the representation gap between multiscale features, inhibiting the ability of the network to aggregate discriminative features. In this article, we propose a novel rich edge features refinement detection network consisting of an encoder–decoder structure that captures rich discriminative edge feature representations by interactively exploring global spatial and channel context. To refine the dense global contextual information in the decoding layer, we propose a bidirectional strip refinement attention (BSRA) adaptively capturing long-range spatial dependency with direction information and long-range channel dependency. BSRA is a lightweight and general module that can be easily inserted into the existing edge detection networks with a negligible computational burden. In addition, we release a polycrystalline solar cell defect edge (PSCDE) dataset that is the first high-quality segmentation database to advance the development of high-quality polycrystalline solar cell manufacturing. Our method achieves an ODS F-measure of 0.854 on the PSCDE dataset with strict evaluation criteria (maxDist = 0.0015), outperforming existing state-of-the-art methods. To further verify the generalization ability of BSRA, we apply BSRA to other edge detection networks, and experiments show that the module further improves the accuracy of these methods on PSCDE.

Most of the existing photovoltaic (PV) defect detection methods are based on the assumption that the training and testing samples satisfy the independent identically distributed. However, in real PV scenarios, the endogenous shift problem exists widely, including background style shift and defect instance shift, which seriously affects the performance of detectors. In this article, we propose a novel network called shift suppression network (SSN) for the endogenous shift of PV defect detection, which consists of two core components, background style suppression (BSS) module and cross-layer graph reasoning (CGR) module. Specifically, BSS uses channel statistics matching alignment to adaptively suppress background style shifts without reliance on unknown domain data. CGR learns semantic dependencies between multiscale channel feature maps through cross-layer interaction, which improves the discriminative ability and localization ability in the face of defect instance shifts. To advance the study of endogenous shift, we provide the first electroluminescence (EL) endogenous shift dataset for PV modules, which creates by three groups of EL images collected at different times, totaling 16 323. The comprehensive evaluation results show that the SSN outperforms the state-of-the-art methods. Furthermore, we design an intelligent defect detection system for PV modules based on SSN. The system has higher detection accuracy and an intuitive visual interface, which can meet the actual detection function and requirements of the production line.

The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD1, 2, 3) dataset for polycrystalline solar cell, which contains 36 543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly free images and anomalous images with ten different categories. Moreover, 37 380 ground truth bounding boxes are provided for eight types of defects. We also carry out a comprehensive evaluation of the state-of-the-art object detection methods based on deep learning. The evaluation results on this dataset provide the initial benchmark, which is convenient for follow-up researchers to conduct experimental comparisons. To the best of our knowledge, this is the first public dataset for PV solar cell anomaly detection that provides box-wise ground truth. Furthermore, this dataset can also be used for the evaluation of many computer vision tasks such as few-shot detection, one-class classification, and anomaly generation.

The surface of a multicrystal solar cell shows multiple crystal grains of random shapes and sizes. It creates an inhomogeneous texture in the surface, which brings great difficulty to automatic crack detection of polycrystalline solar surface. As a perceptual grouping approach, tensor voting can extract curvilinear structures such as lines and curves from noisy, binary data in 2-D or 3-D, without invoking specific object or model. However, traditional tensor voting can be susceptible to the gap problem and structural noise. To address the problems mentioned above, a new iterative tensor voting algorithm is presented, which efficacy bases on iterative refinements of the curvilinear structures. By combining the proximity and continuity of Gestalt principles, in each iteration step, a new decay function is redefined according to the difference of angle between the voter and receiver to rebuild the voting field, which makes the points that lie on curvilinear structures vote more information (a bigger tensor) to the ones with the same attribute. The proposed method can solve the gap problem and is robust to structural noises. The experimental results show that the proposed method can detect crack on the inhomogeneous textured surface and achieve an average detection rate of 95.2% on the industrial data set. Note to Practitioners-Automatic vision-based defect detection on the solar cell is difficult due to inhomogeneous texture and low contrast between defects and background in the surface. In order to solve these problems, by combining the proximity and continuity of Gestalt principles, this article proposed a new iterative tensor voting algorithm which can refine the curvilinear structures with iterations. Experiments have shown that the proposed method can detect crack under the interference of inhomogeneous texture and complex background.

The automatic defects detection for solar cell electroluminescence (EL) images is a challenging task, due to the similarity of defect features and complex background features. To address this problem, in this article a novel complementary attention network (CAN) is designed by connecting the novel channel-wise attention subnetwork with spatial attention subnetwork sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features. In CAN, the novel channel-wise attention subnetwork applies convolution operation to integrate the concatenated and discriminative output features extracted by global average pooling layer and global max pooling layer, which can make fully use of these informative features. Furthermore, a region proposal attention network (RPAN) is proposed by embedding CAN into region proposal network in faster R-CNN (convolution neutral network) to extract more refined defective region proposals, which is used to construct a novel end-to-end faster RPAN-CNN framework for detecting defects in raw EL image. Finally, some experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method performs much better than other methods in terms of defects classification and detection results in raw solar cell EL images.

Automatic defect detection on strip steel surfaces is a challenging task in computer vision, owing to miscellaneous patterns of defects, disturbance of pseudodefects, and random arrangement of gray-level in background. In this paper, a novel template establishment is presented. Further, a simple guidance template-based algorithm for strip steel surface defect detection is proposed. First, a large number of defect-free images are collected to obtain the statistical characteristic of normal textures. Second, for each given test image, the initial template is built according to the statistical characteristic and the size of test image. Then, a sorting operation is applied to the given test image. Further, by updating the initial template, a unique guidance template is generated based on specific intensity distribution of the sorted test image. So far, the background of each test image is approximately reconstructed in the guidance template. Finally, based on pixel-wise detection, the defects can be located accurately by subtraction operation between the guidance template and sorted test image, reverse sorting operation, and adaptive threshold determination. Experimental results show that the proposed method is both efficient and effective. It achieves a better average detection rate of 96.2% on a data set including 1500 test images.