In semiconductor chip manufacturing, defect detection is a crucial step for ensuring product quality and process stability. However, obtaining defect images in actual production is highly challenging, as most images are defect-free. Such data imbalance may result in missed or incorrect detections in downstream tasks, thereby adversely affecting the semiconductor chip's fabrication process and its final quality. To address this issue, we propose the "Spatial Anomaly Embedding Diffusion (SAE-Diffu)" model. This model incorporates domain knowledge of semiconductor chip fabrication and integrates a spatial anomaly embedding module. It generates defect images that exhibit both domain realism and diversity, while also enhancing the focus on small-target defects and enabling controllability of the defect regions. Furthermore, to improve the model's efficiency, we introduce a condition-guided single-step denoising mechanism. This mechanism reduces the number of iterations while ensuring the quality of the generated defect images is maintained. The experimental results clearly demonstrate that SAE-Diffu generates defect images which are superior in terms of realism and diversity when compared to existing methods. This serves as strong validation of the model's effectiveness and applicability within the semiconductor domain.

To solve the problem that the depth-of-field limitation of optical imaging systems leads to the inability of a single image to capture the clarity of the entire scene, we propose FADFNet,a Focus Attention and Dual-Feature Adaptation Multi-Focus Image Fusion Network. Traditional fusion methods rely on handcrafted features such as gradients or contrasts, which usually make it difficult to distinguish between focused and defocus areas in complex scenes, while deep learning methods often produce artifacts or lose details in transition areas.FADFNet overcomes these challenges by integrating three key components: the Focus Attention (FA) block, which captures pixel-level global dependencies and dynamically adjusts feature importance through an adaptive attention mechanism; the Dual-Feature Adaptive (DA) block, which employs a bidirectional structure to globally weight and embed focused pixels, thereby enhancing focused region features; and the Feature Pyramid Network (FPN), which modulates near- and far-focus feature maps to produce a high-resolution fused images. The experimental results show that FADFNet is superior to existing methods in terms of fusion quality.

Fabric defect detection plays a pivotal role in achieving intelligent quality control within textile manufacturing. However, intricate background textures, the high visual similarity between defects and the background, and the low proportion of small defects in high-resolution images impede detection accuracy. To address these challenges, we introduce Fabric-DETR, an efficient model for multi-scale fabric defect detection in complex environments. This network integrates three key modules: the Bottle Neck Conv2X Block for enhanced backbone feature extraction, Dynamic Attention-based Intra-scale Feature Interaction to improve attention to small targets, and the Zoom Diffuse Pyramid Network for efficient multi-scale feature fusion. Experiments on a six-class fabric defect dataset demonstrate that Fabric-DETR outperforms existing state-of-the-art methods, achieving a 94.8% mAP, representing improvements of 2.8%, 2.8%, 1.9%, 6.1%, 3.1%, and 2.5% over RT-DETR, YOLOv5-m, YOLOv8-m, YOLOv10-m, YOLOv11-m, and YOLOv12-m, respectively.

The Segment Anything Model (SAM) lacks adaptability to specific industrial domains, hindering its ability to leverage process-specific knowledge for adaptive optimization. Furthermore, its performance on segmenting low-contrast targets is suboptimal, causing blurred or imprecise boundary delineation of subtle defects. Finally, the reliance on manual prompt inputs limits its suitability for the fully automated, high-efficiency inspection requirements of industrial production. To address these limitations, this study introduces the Industrial Defect-SAM (ID-SAM) for industrial defect segmentation. Initially, we incorporate expert knowledge and industrial process data to construct a structured prior knowledge base of defect patterns. This knowledge is then transformed into vectors via a Contrastive Language-Image Pretraining (CLIP) mechanism, enabling the model to utilize domain-specific knowledge for adaptive optimization. Subsequently, we employ the global feature fusion, integrating the prior knowledge vectors generated by CLIP with the raw features from the image encoder. This joint modeling approach, leveraging cross-modal alignment, enhances the segmentation accuracy of low-contrast targets. Finally, a CLIP-based semantic mechanism is designed that utilizes cross-modal semantic understanding to enable the model to automatically perceive target regions, eliminating the need for manual prompts, thereby improving inspection efficiency. Experimental results demonstrate that the ID-SAM effectively enhances the segmentation accuracy of subtle defects and improves generalization capabilities across different process environments in semiconductor chip defect detection tasks.

Magnetic Flux Leakage (MFL) detection has emerged as a promising nondestructive testing method for identifying defects in ferromagnetic materials, such as the steel cables used in cable-stayed bridges. Focusing on these critical load-bearing components—which are prone to corrosion, cracks, and localized damage under harsh environmental and operational conditions—this study leverages MFL-based point cloud data to enhance defect detection accuracy and reliability. A specialized MFL sensor array was deployed to collect high-resolution three-dimensional leakage magnetic field data, generating dense point clouds that capture the spatial variations in magnetic flux induced by surface and subsurface anomalies. To isolate defect-related signals from background interference, advanced point cloud processing algorithms were developed, encompassing noise filtering, feature extraction, and geometric reconstruction. Crucially, the surface area of the reconstructed 3D point clouds serves as a quantitative metric for defect detection. Furthermore, the local metal loss area of the steel cable can be derived from the cross-sectional surface area of these 3D point clouds. The results demonstrate that integrating point cloud data with automated defect characterization provides a robust framework for the structural health monitoring of stay cables, enabling early intervention and prolonging service life. This work contributes significantly to advancing intelligent inspection technologies for critical steel cable infrastructure.

This paper presents a decentralized cooperative motion planning approach for surface inspection of 3D structures which includes uncertainties like size, number, shape, position, using multi-robot systems (MRS). Given that most of existing works mainly focus on surface inspection of single and fully known 3D structures, our motivation is two-fold: first, 3D structures separately distributed in 3D environments are complex, therefore the use of MRS intuitively can facilitate an inspection by fully taking advantage of sensors with different capabilities. Second, performing the aforementioned tasks when considering uncertainties is a complicated and time-consuming process because we need to explore, figure out the size and shape of 3D structures and then plan surface-inspection path. To overcome these challenges, we present a meta-learning approach that provides a decentralized planner for each robot to improve the exploration and surface inspection capabilities. The experimental results demonstrate our method can outperform other methods by approximately 10.5%-27% on success rate and 70%-75% on inspection speed.

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural network with novel attention mechanism modules. We propose an effective channel and spatial Residual Squeeze-and-Excitation (csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiments with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested a few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.

Multi-agent path finding in dynamic environments is of great academic and practical value for multi-robot systems in the real world. To improve the effectiveness and efficiency of the learning process during path planning in dynamic environments, we introduce an algorithm called Attention and BicNet based Multi-agent path planning with effective reinforcement (AB-Mapper) under the actor-critic reinforcement learning framework. In this framework, on one hand, we design an actor-network that can utilize the BicNet with communication function to achieve the intra-team coordination. On the other hand, we propose a critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by considering the behaviours of its surrounding agents. Compared with the SOTA method Mapper in crowded environments with dynamic obstacles, our AB-Mapper is more effective (90.27±0.06% vs. 61.65±13.90% in terms of mean success rate) in solving the general multi-agent path finding problem.

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.