Search Results (487)

To address the challenges of low illumination, heavy dust, and severe occlusion in fully mechanized mining faces, this paper proposes a shearer drum detection algorithm named DCS-YOLO. To enhance the model’s ability to effectively capture features under drum deformation and occlusion, a C3k2_DCNv4 module based on deformable convolution (DCNv4) is incorporated into the network. This module adaptively adjusts convolution sampling points according to the drum’s size and position, enabling efficient and precise multi-scale feature extraction. To overcome the limitations of conventional convolution in global feature modeling, a convolution and attention fusion module (CAFM) is constructed, which combines lightweight convolution with attention mechanisms to selectively reweight feature maps at different resolutions. Under low-light conditions, the Shape-IoU loss function is employed to achieve accurate regression of irregular drum boundaries while considering both positional and shape similarity. In addition, GSConv is adopted to achieve model lightweighting while maintaining efficient feature extraction capability. Experiments were conducted on a dataset built from shearer drum images collected in underground coal mines. The results demonstrate that, compared with YOLOv11n, the proposed method reduces Params and Flops by 7.7% and 4.6%, respectively, while improving precision, recall, mAP@0.5, and mAP@0.5:0.95 by 2.9%, 3.2%, 1.1%, and 3.3%, respectively. These findings highlight the significant advantages of the proposed approach in both model lightweighting and detection performance. Full article

Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated Recurrent Units (GRUs) for temporal sequence modeling. The framework leverages long-sequence prototype monitoring data, including reservoir level, temperature, and displacement, to capture complex spatiotemporal interactions between environmental conditions and dam behavior. A parameter optimization strategy is further incorporated to refine the model’s architecture and hyperparameters. Experimental evaluations on real-world hydropower station datasets demonstrate that the proposed CNN–GRU model outperforms conventional statistical and machine learning methods, achieving an average determination coefficient of R² = 0.9582 with substantially reduced prediction errors (RMSE = 4.1121, MAE = 3.1786, MAPE = 3.1061). Both qualitative and quantitative analyses confirm that CNN–GRU not only provides stable predictions across multiple monitoring points but also effectively captures sudden deformation fluctuations. These results underscore the potential of the proposed AI-assisted framework as a robust and reliable tool for intelligent monitoring, safety assessment, and early warning in large-scale hydropower facilities. Full article

Aging water pipelines are increasingly prone to structural failure, leakage, and ground subsidence, creating critical risks to urban infrastructure. Closed-circuit television endoscopy is widely used for internal assessment, but it depends on manual interpretation and lacks reliable quantitative defect information. Traditional vanishing point detection techniques, such as the Hough Transform, often fail under practical conditions due to irregular lighting, debris, and deformed pipe surfaces, especially when pipes are water-filled. To overcome these challenges, this study introduces a deep learning-based method that estimates inverse projection parameters from monocular endoscopic images. The proposed approach reconstructs a spatially accurate two-dimensional projection of the pipe interior from a single frame, enabling defect quantification for cracks, scaling, and delamination. This eliminates the need for stereo cameras or additional sensors, providing a robust and cost-effective solution compatible with existing inspection systems. By integrating convolutional neural networks with geometric projection estimation, the framework advances computational intelligence applications in pipeline condition monitoring. Experimental validation demonstrates high accuracy in pose estimation and defect size recovery, confirming the potential of the system for automated, non-disruptive pipeline health evaluation. Full article

Accurate detection of strip steel surface defects is a critical step to ensure product quality and prevent potential safety hazards. In practical inspection scenarios, defects on strip steel surfaces typically exhibit sparse distributions, diverse morphologies, and irregular shapes, while background regions dominate the images, exhibiting highly similar texture characteristics. These characteristics pose challenges for detection algorithms to efficiently and accurately localize and extract defect features. To address these challenges, this study proposes a Supervised Focused Feature Network for steel strip surface defect detection. Firstly, the network constructs a supervised range based on annotation information and introduces supervised convolution operations in the backbone network, limiting feature extraction within the supervised range to improve feature learning effectiveness. Secondly, a supervised deformable convolution layer is designed to achieve adaptive feature extraction within the supervised range, enhancing the detection capability for irregularly shaped defects. Finally, a supervised region proposal strategy is proposed to optimize the sample allocation process using the supervised range, improving the quality of candidate regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 81.2% on the NEU-DET dataset and 72.5% mAP on the GC10-DET dataset. Ablation studies confirm the contribution of each proposed module to feature extraction efficiency and detection accuracy. Results indicate that the proposed network effectively enhances the efficiency of sparse defect feature extraction and improves detection accuracy. Full article

Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing language rules remains a complex problem. This paper addresses two key challenges: (1) The over-correction behavior of language models, particularly on semantically deficient input, can result in both recognition errors and loss of critical information. (2) Character misalignment in visual features, which affects recognition accuracy. To address these problems, we propose a Deformable-Alignment-based Dual Correction Mechanism (DADCM) for STR. Our method includes the following key components: (1) We propose a visually guided and language-assisted correction strategy. A dynamic confidence threshold is used to control the degree of language model intervention. (2) We designed a visual backbone network called SCRTNet. The net enhances key text regions through a channel attention module (SENet) and applies deformable convolution (DCNv4) in deep layers to better model distorted or curved text. (3) We propose a deformable alignment module (DAM). The module combines Gumbel-Softmax-based anchor sampling and geometry-aware self-attention to improve character alignment. Experiments on multiple benchmark datasets demonstrate the superiority of our approach. Especially on the Union14M-Benchmark, where the recognition accuracy surpasses previous methods by 1.1%, 1.6%, 3.0%, and 1.3% on the Curved, Multi-Oriented, Contextless, and General subsets, respectively. Full article

Underwater wireless optical communication (UWOC) is a key technology in ocean resource development, and its link stability is often limited by the difficulty of optical alignment in complex underwater environments. In response to this difficulty, this study has focused on improving the Real-Time Detection Transformer v2 (RT-DETRv2) model. We have improved the underwater light source detection model by collaboratively designing a lightweight backbone network and deformable convolution, constructing a cross-stage local attention mechanism to reduce the number of network parameters, and introducing geometrically adaptive convolution kernels that dynamically adjust the distribution of sampling points, enhance the representation of spot-deformation features, and improve positioning accuracy under optical interference. To verify the effectiveness of the model, we have constructed an underwater light-emitting diode (LED) light-spot detection dataset containing 11,390 images was constructed, covering a transmission distance of 15–40 m, a ±45° deflection angle, and three different light-intensity conditions (noon, evening, and late night). Experiments show that the improved model achieves an average precision at an intersection-over-union threshold of 0.50 (AP50) value of 97.4% on the test set, which is 12.7% higher than the benchmark model. The UWOC system built based on the improved model achieves zero-bit-error-rate communication within a distance of 30 m after assisted alignment (an initial lateral offset angle of 0°–60°), and the bit-error rate remains stable in the 10⁻⁷–10⁻⁶ range at a distance of 40 m, which is three orders of magnitude lower than the traditional Remotely Operated Vehicle (ROV) underwater optical communication system (a bit-error rate of 10⁻⁶–10⁻³), verifying the strong adaptability of the improved model to complex underwater environments. Full article

Object detection has a vital impact on the analysis and interpretation of visual scenes. It is widely utilized in various fields, including healthcare, autonomous driving, and vehicle surveillance. However, complex scenes containing small, occluded, and multiscale objects present significant difficulties for object detection. This paper introduces a lightweight object detection algorithm, utilizing YOLOv8n as the baseline model, to address these problems. Our method focuses on four steps. Firstly, we add a layer for small object detection to enhance the feature expression capability of small objects. Secondly, to handle complex forms and appearances, we employ the C2f-DCNv2 module. This module integrates advanced DCNv2 (Deformable Convolutional Networks v2) by substituting the final C2f module in the backbone. Thirdly, we designed the CBAM, a lightweight attention module. We integrate it into the neck section to address missed detections. Finally, we use Ghost Convolution (GhostConv) as a light convolutional layer. This alternates with ordinary convolution in the neck. It ensures good detection performance while decreasing the number of parameters. Experimental performance on the PASCAL VOC dataset demonstrates that our approach lowers the number of model parameters by approximately 9.37%. The mAP@0.5:0.95 increased by 0.9%, recall (R) increased by 0.8%, mAP@0.5 increased by 0.3%, and precision (P) increased by 0.1% compared to the baseline model. To better evaluate the model’s generalization performance in real-world driving scenarios, we conducted additional experiments using the KITTI dataset. Compared to the baseline model, our approach yielded a 0.8% improvement in mAP@0.5 and 1.3% in mAP@0.5:0.95. This result indicates strong performance in more dynamic and challenging conditions. Full article

The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Persistent scatterer (PS) pixel selection is crucial in the PS-InSAR technique, ensuring the quality and quantity of PS pixels for accurate deformation measurements. However, traditional methods like the amplitude dispersion index (ADI)-based method struggle to balance the quality and quantity of PS pixels. To adequately select high-quality PS pixels, and thus improve the deformation measurement performance of PS-InSAR, the multi-temporal feature extraction network (MFN) is constructed in this paper. The MFN combines the 3D U-Net and the convolutional long short-term memory (CLSTM) to achieve time-series analysis. Compared with traditional methods, the proposed MFN can fully extract the spatiotemporal characteristics of complex SAR images to improve PS pixel selection performance. The MFN was trained with datasets constructed by reliable PS pixels estimated by the ADI-based method with a low threshold using ∼350 time-series Sentinel-1A SAR images, which contain man-made objects, farmland, parkland, wood, desert, and waterbody areas. To test the validity of the MFN, a deformation measurement experiment was designed for Tongzhou District, Beijing, China with 38 SAR images obtained by Sentinel-1A. Moreover, the similar time-series interferometric pixel (STIP) index was introduced to evaluate the phase stability of selected PS pixels. The experimental results indicate a significant improvement in both the quality and quantity of selected PS pixels, as well as a higher deformation measurement accuracy, compared to the traditional ADI-based method. Full article

Reducing hydrodynamic resistance remains a central concern in modern ship design. The Simulation-Based Design technique offers high-fidelity optimization through computational fluid dynamics, but this comes at the cost of computational efficiency. In contrast, surrogate models trained offline can accelerate the process but often compromise on accuracy. To address this issue, this study proposes a hybrid optimization framework connecting a computational fluid dynamics solver and a convolutional neural network surrogate model within a dual-mode mechanism. By comparing selected computational fluid dynamics evaluations with surrogate predictions during each iteration, the system is able to balance the precision and efficiency adaptively. The framework integrates a particle swarm optimizer, a free-form deformation modeler, and a dual-mode solver. Case studies on three benchmark hulls including KCS, KVLCC1, and JBC have shown 3.40%, 3.95%, and 2.74% resistance reduction, respectively, with computation efficiency gains exceeding 44% compared to the traditional Simulation-Based Design process using full computational fluid dynamics. This study provides a practical attempt to enhance the efficiency of hull form optimization while maintaining accuracy. Full article

Landslides constitute major geohazards that threaten human life, property, and ecological environments; it is imperative to acquire their location information accurately and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) has been demonstrated to be capable of acquiring subtle surface deformation with high precision and is widely applied to wide-area landslide detection. However, after obtaining InSAR deformation rates, visual interpretation is conventionally employed in landslide detection, which is characterized by significant temporal consumption and labor-intensive demands. Despite advancements that have been made through cluster analysis, hotspot analysis, and deep learning, persistent challenges such as low intelligence levels and weak generalization capabilities remain unresolved. In this study, we propose an improved Faster R-CNN model to achieve automatic detection of slow-moving landslides based on InSAR Line of Sight (LOS) annual rates in the upper and middle reaches of the Jinsha River Basin. The model incorporates a ResNet-34 backbone network, Feature Pyramid Network (FPN), and Convolutional Block Attention Module (CBAM) to effectively extract multi-scale features and enhance focus on subtle surface deformation regions. This model achieved test set performance metrics of 93.56% precision, 97.15% recall, and 93.6% F1-score. The proposed model demonstrates robust detection performance for slow-moving landslides, and through comparative analysis with the detection results of hotspot analysis and K-means clustering, it is verified that this method has strong generalization ability in the representative landslide-prone areas of the Qinghai–Tibet Plateau. This approach can support dynamic updates of regional slow-moving landslide inventories, providing crucial technical support for the detection of landslides. Full article

Safety helmets and gloves are indispensable personal protective equipment in power grid operation environments. Traditional detection methods for safety helmets and gloves suffer from reduced accuracy due to factors such as dense personnel presence, varying lighting conditions, occlusions, and diverse postures. To enhance the detection performance of safety helmets and gloves in power grid operation environments, this paper proposes a novel algorithm, YOLO-DCRCF, based on YOLO11 for detecting the wearing of safety helmets and gloves in such settings. By integrating Deformable Convolutional Network version 2 (DCNv2), the algorithm enhances the network’s capability to model geometric transformations. Additionally, a recalibration feature pyramid (RCF) network is innovatively designed to strengthen the interaction between shallow and deep features, enabling the network to capture multi-scale information of the target. Experimental results show that the proposed YOLO-DCRCF model achieved mAP50 scores of 92.7% on the Safety Helmet Wearing Dataset (SHWD) and 79.6% on the Safety Helmet and Gloves Wearing Dataset (SHAGWD), surpassing the baseline YOLOv11 model by 1.1% and 2.7%, respectively. These results meet the real-time safety monitoring requirements of power grid operation sites. Full article

Sandwich panels are widely used in industrial roofing due to their lightweight and thermal insulation properties; however, their structural fire resistance remains insufficiently understood. This study presents a data-driven approach to predict the mid-span deformation of glass wool-cored sandwich roof panels subjected to ISO 834-5 standard fire tests. A total of 39 full-scale furnace tests were conducted, yielding 1519 data points that were utilized to develop deep learning models. Feature selection identified nine key predictors: elapsed time, panel orientation, and seven unexposed-surface temperatures. Three deep learning architectures—convolutional neural network (CNN), multilayer perceptron (MLP), and long short-term memory (LSTM)—were trained and evaluated through rigorous 5-fold cross-validation and independent external testing. Among them, the CNN approach consistently achieved the highest accuracy, with an average cross-validation performance of $R^2=0.91(\mathrm{mean}\mathrm{absolute}\mathrm{error}\left(\mathrm{MAE}\right)=4.40;\mathrm{root}\mathrm{mean}\mathrm{square}\mathrm{error}\left(\mathrm{RMSE}\right)=6.42)$, and achieved $R^2=0.76(\mathrm{MAE}=6.52,\mathrm{RMSE}=8.62)$ on the external test set. These results highlight the robustness of CNN in capturing spatially ordered thermal–structural interactions while also demonstrating the limitations of MLP and LSTM regarding the same experimental data. The findings provide a foundation for integrating machine learning into performance-based fire safety engineering and suggest that data-driven prediction can complement traditional fire-resistance assessments of sandwich roofing systems. Full article

Accurately identifying fractures from X-ray images is crucial for timely and appropriate medical treatment. However, existing models suffer from problems of false localization and poor accuracy. Therefore, this research proposes a medical X-ray fracture detection model with precise localization based on the You Only Look Once version 11 nano (YOLOv11n) model. Firstly, a data augmentation technique combining random rotation, translation, flipping and content recognition padding is designed to expand the public dataset, alleviating the overfitting risk due to scarce medical imaging data. Secondly, a Bone-Multi-Scale Convolutional Attention (Bone-MSCA) module, designed by combining multi-directional convolution, deformable convolution, edge enhancement and channel attention, is introduced into the backbone network. It can capture fracture area features, explore multi-scale features and enhance attention to spatial details. Finally, the Focal mechanism is combined with Smoothed Intersection over Union (Focal-SIoU) as the loss function to enhance sensitivity to small fracture areas by adjusting sample weights and optimizing direction perception. Experimental results show that the improved model trained with the expanded dataset outperforms other mainstream single-object detection models. Compared with YOLOv11n, its detection accuracy, recall rate, F1-Score and mean Average Precision 50 increase by 4.33%, 0.92%, 2.52% and 1.24%, respectively, reaching 93.56%, 86.29%, 89.78% and 92.88%. Visualization of the results verifies its high accuracy and positioning ability in medical X-ray fracture detection. Full article