Search Results (12)

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in simultaneously achieving both high-precision and high-speed requirements. To address this, this study proposes the real-time defect detection algorithm FDC-YOLO, enabling precise and efficient identification of blurred defects. We design the dynamic subtractive attention sampling module (DSAS) to dynamically compensate for information discrepancies during sampling, which reduces critical information loss caused by multi-scale feature fusion. We design a high-frequency information processing module (HFM) to enhance defect feature representation in the frequency domain, which significantly improves the visibility of defect regions while mitigating blur-induced noise interference. Additionally, we design a classification domain detection head (CDH) to focus on domain-invariant features across categories. Finally, FDC-YOLO achieves 7.9% and 3.5% mAP improvements on the aero-engine blade defect dataset and low-resolution NEU-DET dataset, respectively, with only 2.68 M parameters and 7.0G FLOPs. These results validate the algorithm’s generalizability in addressing low-accuracy issues across diverse blur artifacts in defect detection. Furthermore, this algorithm is combined with the tensegrity continuum robot to jointly construct an automatic defect detection system for aircraft engines, providing an efficient and reliable innovative solution to the problem of internal damage detection in engines. Full article

Natural gas pipeline leak monitoring suffers from severe environmental noise, non-stationary signals, and complex multi-source variable couplings, limiting prediction accuracy and robustness. Inspired by biological perceptual systems, particularly their multimodal integration and dynamic attention allocation, we propose GL-TransLSTM, a biomimetic hybrid deep learning model. It synergistically combines Transformer’s global self-attention (emulating selective focus) and LSTM’s gated memory (mimicking neural temporal retention). The architecture incorporates a multimodal fusion pipeline; raw sensor data are first decomposed via CEEMDAN to extract multi-scale features, then processed by an enhanced LSTM-Transformer backbone. A novel physics-informed gated attention mechanism embeds gas diffusion dynamics into attention weights, while an adaptive sliding window adjusts temporal granularity. This study makes evaluations on an industrial dataset with methane concentration, temperature, and pressure, GL-TransLSTM achieves 99.93% accuracy, 99.86% recall, and 99.89% F1-score, thereby significantly outperforming conventional LSTM and Transformer-LSTM baselines. Experimental results demonstrate that the proposed biomimetic framework substantially enhances modeling capacity and generalization for non-stationary signals in noisy and complex industrial environments through multi-scale fusion, physics-guided learning, and bio-inspired architectural synergy. Full article

Semantic segmentation of urban scenes from red–green–blue and thermal infrared imagery enables per-pixel categorization, delivering precise environmental understanding for autonomous driving and urban planning. However, existing methods suffer from inefficient fusion and insufficient boundary accuracy due to modal differences. To address these challenges, we propose a bidirectional dynamic adaptation framework with two complementary networks. The modality-aware network uses dual attention and multi-scale feature integration to balance modal contributions adaptively, improving intra-class semantic consistency and reducing modal disparities. The edge-texture guidance network applies pixel-level and feature-level weighting with Sobel and Gabor filters to enhance inter-class boundary discrimination, improving detail and boundary precision. Furthermore, the framework redefines multi-modal synergy using an adaptive cross-modal mutual learning mechanism. This mechanism employs information-driven dynamic alignment and probability-guided semantic consistency to overcome the fixed constraints of traditional mutual learning. This cohesive orchestration enhances multi-modal fusion efficiency and boundary delineation accuracy. Extensive experiments on the MFNet and PST900 datasets demonstrate the framework’s superior performance in urban road, vehicle, and pedestrian segmentation, surpassing state-of-the-art approaches. Full article

Radionuclide tracer technology, as a state-of-the-art tool for quantifying and monitoring soil erosion processes, has attracted much attention in global sustainable land management research in recent years. However, existing studies are fragmented in methodological applications, lack systematic knowledge integration and interdisciplinary perspectives, and lack global research trends and dynamic evolution of key themes. This study integrates Bibliometrix, VOSviewer, and CiteSpace to conduct bibliometric and knowledge mapping analysis of 1692 documents (2000–2023) in the Web of Science Core Collection, focusing on the overall developmental trends, thematic evolution, and progress of convergence and innovation. The main findings of the study are as follows: (1) China, the United States, and the United Kingdom are in a “three-legged race” at the national level, with China focusing on technological application innovation, the United States on theoretical breakthroughs, and the United Kingdom contributing significantly to methodological research; (2) “soil erosion” and “¹³⁷Cs” continue to be the core themes, while “climate change” and “human impact” on soil erosion and its reflection in radionuclide tracing became the focus of attention; and (3) multi-scale radionuclide tracing (watershed, slope), multi-method synergy (radionuclide tracing combined with RS, GIS, AI), and the integration of advanced measurement and control technologies (PGS, ARS) have become cutting-edge trends in soil erosion monitoring and control. This study provides three prospective research directions—the construction of a global soil erosion database, the policy transformation mechanism of the SDG interface, and the iterative optimization of multi-radionuclide tracer technology, which will provide scientific guidance for the realization of the sustainable management of soil erosion and the goal of zero growth of land degradation globally. Full article

Fish are a vital aquatic resource worldwide, and the sustainable development of aquaculture is essential for global food security and economic growth. However, the high incidence of fish diseases in complex aquaculture environments significantly hampers sustainability, and traditional manual diagnosis methods are inefficient and often inaccurate. To address the challenges of small-lesion detection, lesion area size and morphological variation, and background complexity, we propose YOLO-TPS, a high-precision fish-disease detection model based on an improved YOLOv11n architecture. The model integrates a multi-module synergy strategy and a triple-attention mechanism to enhance detection performance. Specifically, the SPPF_TSFA module is introduced into the backbone to fuse spatial, channel, and neuron-level attention for better multi-scale feature extraction of early-stage lesions. A PC_Shuffleblock module incorporating asymmetric pinwheel-shaped convolutions is embedded in the detection head to improve spatial awareness and texture modeling under complex visual conditions. Additionally, a scale-aware dynamic intersection over union (SDIoU) loss function was designed to accommodate changes in the scale and morphology of lesions at different stages of the disease. Experimental results on a dataset comprising 4596 images across six fish-disease categories demonstrate superior performance (mAP_0.5: 97.2%, Precision: 97.9%, Recall: 95.1%) compared to the baseline. This study offers a robust, scalable solution for intelligent fish-disease diagnosis and has promising implications for sustainable aquaculture and animal health monitoring. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet. Full article

Aiming at the problem of appearance drift and susceptibility to noise interference in visual place recognition (VPR), we propose DD–DPFE: a Dynamic Difference and Dual-Path Feature Enhancement method. Embedding differential attention mechanisms in the DINOv2 model to mitigate the effects of process interference and adding serial-parallel adapters allows efficient model parameter migration and task adaptation. Our method constructs a two-way feature enhancement module with global–local branching synergy. The global branch employs a dynamic fusion mechanism with a multi-layer Transformer encoder to strengthen the structured spatial representation to cope with appearance changes, while the local branch suppresses the over-response of redundant noise through an adaptive weighting mechanism and fuses the contextual information from the multi-scale feature aggregation module to enhance the robustness of the scene. The experimental results show that the model architecture proposed in this paper is an obvious improvement in different environmental tests. This is most obvious in the simulation test of a night scene, verifying that the proposed method can effectively enhance the discriminative power of the system and its anti-jamming ability in complex scenes. Full article

The increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the combined effects of light absorption and scattering, resulting in color imbalance, low contrast, and illumination instability. These factors limit the effectiveness of visual-based autonomous operations. We propose ATS-UGAN, a Dual-domain Adaptive Synergy Generative Adversarial Network for low-light underwater image enhancement to confront the above issues. The network integrates Multi-scale Hybrid Attention (MHA) that synergizes spatial and frequency domain representations to capture key image features adaptively. An Adaptive Parameterized Convolution (AP-Conv) module is introduced to handle non-uniform scattering by dynamically adjusting convolution kernels through a multi-branch design. In addition, a Dynamic Content-aware Markovian Discriminator (DCMD) is employed to perceive the dual-domain information synergistically, enhancing image texture realism and improving color correction. Extensive experiments on benchmark underwater datasets demonstrate that ATS-UGAN surpasses state-of-the-art approaches, achieving 28.7/0.92 PSNR/SSIM on EUVP and 28.2/0.91 on UFO-120. Additional reference and no-reference metrics further confirm the improved visual quality and realism of the enhanced images. Full article

To address key challenges in clothing trademark quality inspection—namely, insufficient defect samples, unstable performance in complex industrial environments, and low detection efficiency—this paper proposes DLF-YOLO, an enhanced YOLOv11-based model optimized for industrial deployment. To mitigate the problem of limited annotated data, an unsupervised generative network, CycleGAN, is employed to synthesize rare defect patterns and simulate diverse environmental conditions (e.g., rotation, noise, and contrast variations), thereby improving data diversity and model generalization. To reduce the impact of industrial noise, a novel multi-scale dynamic synergy attention (MDSA) attention mechanism is introduced, which utilizes dual attention in both channel and spatial dimensions to focus more accurately on key regions of the trademark, effectively suppressing false detections caused by lighting variations and fabric textures. Furthermore, the high-level selective feature pyramid network (HS-FPN) module is adopted to make the neck structure more lightweight, where the feature selection sub-module enhances the perception of fine edge defects, while the feature fusion sub-module achieves a balance between model lightweighting and detection accuracy through the aggregation of hierarchical multi-scale context information. In the backbone, DWConv replaces standard convolutions before the C3k2 module to reduce computational complexity, and HetConv is integrated into the C3k2 module to simultaneously reduce computational cost and enhance feature extraction capabilities, achieving the goal of maintaining model accuracy. Experimental results on a custom-built dataset demonstrate that DLF-YOLO achieves an mAP@0.5:0.95 of 80.2%, with a 49.6% reduction in parameters and a 25.6% reduction in computational load compared to the original YOLOv11. These results highlight the potential of DLF-YOLO as a scalable and efficient solution for lightweight, industrial-grade defect detection in clothing trademarks. Full article

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse polyp morphologies, and ambiguous boundaries make this task difficult. To address these issues, we propose the Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: the Multi-Granularity Hybrid Filtering (MGHF) module for extracting multi-scale contextual information, the Dynamic Granularity Partition Synergy (DGPS) module for enhancing polyp-background differentiation through adaptive feature interaction, and the Multi-Granularity Rough Attention (MGRA) mechanism for further optimizing boundary recognition. Extensive experiments on the ColonDB and CVC-300 datasets demonstrate that S-MGRAUNet significantly outperforms existing methods while achieving competitive results on the Kvasir-SEG and ClinicDB datasets, validating its segmentation accuracy, robustness, and generalization capability, all while effectively reducing computational complexity. This study highlights the value of multi-granularity feature extraction and attention mechanisms, providing new insights and practical guidance for advancing multi-granularity theories in medical image segmentation. Full article

Climate-smart agriculture (CSA) responds in order to sustain agriculture under a changing environment, and is a major priority in the development sphere. However, to achieve impact at scale, CSA innovations must address agricultural systems’ context-specific and multi-dimensional nature and be purveyed through feasible scaling processes. Unfortunately, knowledge on the scaling of CSA innovations under smallholder farming systems and in the context of developing countries remains scant. Understanding scaling processes is essential to the design of a sustainable scaling strategy. This study aimed to draw lessons on scaling from 25 cases of scaling CSA, and related projects in Ethiopia, Kenya, Uganda, and Tanzania implemented by public institutions, local and international research organisations, Non-Govermental Orginsations(NGOs), and community-based organisations. Generally, scaling follows a linear pathway comprising technology testing and scaling. Most cases promoted technologies and models geared towards climate change adaptation in crop-based value chains, and only a few cases incorporated mitigation measures. Efforts to engage the private sector involved building business models as a potential scaling pathway. The cases were very strong on capacity building and institutionalisation from local, national, and even regional levels. However, four critical areas of concern about the sustainability of scaling emerged from the study: (i) There is little understanding and capture of the dynamics of smallholder farming systems in scaling strategies; (ii) climate data, projections, and impact models are rarely applied to support the decision of scaling; (iii) considerations for the biophysical and spatial-temporal impacts and trade-offs analysis in scaling is minimal and just starting to emerge; and (iv) there are still challenges effecting systemic change to enable sustainable scaling. In response to these concerns, we propose investment in understanding and considering the dynamics of the smallholder farming system and how it affects adoption, and subsequently scaling. Programme design should incorporate climate change scenarios. Scaling programmes can maximise synergies and leverage resources by adopting a robust partnerships model. Furthermore, understanding the spatio-temporal impact of scaling CSA on ecological functioning deserves more attention. Lastly, scaling takes time, which needs to be factored into the design of programmes. Full article