Search Results (116)

Background/Objectives: Real-time arthroscopic bone drilling in simulators requires high graphics and haptic update rates while deforming bone visually. We present a fully GPU-resident, shader-based pipeline for the Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST) that avoids mesh regeneration. Methods: Instead of changing geometry, the method updates a vector displacement map and a tessellation amount map. The pipeline uses six GPU passes: drawing-vector calculation, displacement-map generation, tessellation-map generation via Sobel edge detection, mipmapped tessellation sampling, vertex displacement, and fragment-level normal correction. A patterned-drawing scheme reduces stretched-triangle artifacts, and an edge-symmetric UV rule assigns identical tessellation factors to shared edges. Results: On a Dell desktop with an Intel Core Ultra 9, NVIDIA GeForce RTX 5090, 32 GB VRAM, 128 GB RAM, and Windows 11, 1000-frame GPU timing showed that 4K texture updates completed in 0.292 ms on average, and the full drilling update completed in 0.397 ms. This is far below the 16.7 ms budget for 60 Hz rendering. The pipeline sustained 120 fps in multi-object scenes and improved frame rate by up to 24% over uniform tessellation, with SSIM ≈ 0.99992 on depth output. Conclusions: The method provides surface deformation, complements volumetric drilling modules, and supports continuous feedback for surgical training. Full article

Oyster mushroom cultivation requires accurate object detection for automated monitoring and precision agriculture applications. This study evaluates a structure-aware three-channel input representation for YOLOv8s in which raw RGB channels are replaced by grayscale intensity, Sobel gradient magnitude, and a complementary structural channel derived from Gaussian Blur, Laplacian of Gaussian (LoG), Canny edge detection, or Gabor filtering. The dataset contains 555 RGB images and 8282 maturity-labeled mushroom instances. Controlled experiments include an RGB baseline with HSV augmentation disabled, component-wise ablations (GGG and GGradG), repeated-seed training, a chronological holdout, and a cross-architecture RT-DETR evaluation. Under the fixed random split, differences among RGB, grayscale, and structure-aware inputs were modest, and the ablations indicate that grayscale conversion accounts for most of the measured effect. Performance decreased substantially under the chronological split, and RT-DETR did not reproduce the same ordering observed with YOLOv8s. These results show that input representation can influence detector behavior, but they do not support a general claim that handcrafted structural channels consistently improve robustness across evaluation protocols or architectures. Full article

Vision-based perception is essential for robotic manipulator grasping because the accuracy of object localization directly affects grasp planning and motion execution. This paper presents a comparative analysis of computer vision algorithms for object detection and localization in a structured robotic pick-and-place scenario. Classical edge detection methods, including Canny, Sobel, Prewitt, and Roberts, were applied to the same RGB image of a manipulator workspace containing box-shaped objects. The algorithms were evaluated using detection accuracy, F1-score, centroid localization error, contour stability, and processing time. The results showed that Canny provided the most reliable performance, detecting all 12 target objects with the highest localization stability. The study confirms that classical edge detection can support lightweight vision-based robotic grasping. Full article

Pixel-level annotation of bi-temporal high-resolution imagery is costly because annotators must distinguish genuine changes from pseudo-changes caused by illumination, seasonality, shadows, and residual misregistration. From a temporal-symmetry perspective, unchanged regions approximately preserve cross-temporal semantic correspondence, whereas genuine building changes introduce localized symmetry breaking between the two acquisition times. This paper presents BSCNet, a semi-supervised framework for binary building change detection that jointly models boundary-sensitive differences and scene-dependent scale preferences. A shared-weight MixTransformer extracts multi-level bi-temporal features. The Edge-Aware Optimization Module suppresses spatially invariant channel responses, enhances residual spatial cues, and predicts a Sobel-supervised edge map. The Parallel Selective Context Module aggregates depthwise-separable branches with different receptive fields and produces an image-level scale distribution. The Multi-scale Edge-Consistent Mean Teacher framework aligns the final prediction, intermediate edge representation, and scale-selection distribution between an exponential-moving-average teacher and the student. Experiments on WHU-CD and LEVIR-CD under 5%, 10%, and 20% labeled-data settings show consistent improvements over RCL, C2F-SemiCD, and CutMix-CD. With 5% labeled data, BSCNet achieves F1/IoU scores of 88.57%/79.49% on WHU-CD and 88.88%/79.98% on LEVIR-CD. An additional UAV-CD evaluation examines transfer to 0.06 m low-altitude UAV imagery containing both building and land changes; under 5% supervision, BSCNet obtains an F1/IoU of 68.07%/51.60%. Progressive ablations confirm complementary gains from the boundary, scale, and consistency components. Full article

To address the challenges of multi-scale target variation, complex background interference, and insufficient feature fusion quality in maritime remote sensing ship detection, this paper proposes MarShip-DET, a frequency-aware multi-scale fusion detection algorithm based on YOLO11n. Three core modules are introduced: Channel-Decoupled Progressive Feature Extraction Module (CDPFEM), which employs asymmetric channel decoupling with dual-statistic channel attention and image-relative-position-encoded multi-head self-attention to enhance discriminative feature extraction; Edge-Aware Region Context Fusion Module (EARCFusion), which integrates learnable Sobel edge sensing and cross-attention correction to achieve precise foreground refinement; and Wavelet-guided Prototype Attention Module (WavePAM), which combines Haar wavelet frequency decomposition with prototype-guided spatial compression attention to strengthen deep semantic representation. Experiments on HRSC2016 demonstrate that MarShip-DET achieves an mAP50 of 94.9% and an mAP50-95 of 84.4%, improving by 3.7% and 4.3% over the baseline, respectively. Zero-shot experiments on HRSID and SSDD, including comparisons with YOLO11n, D-FINE-N, and YOLOv13n, provide additional evidence of cross-domain transferability under the evaluated protocol. Full article

Alzheimer’s disease (AD) is a neurodegenerative disorder that causes progressive damage to brain neurons, leading to declines in cognitive and behavioral abilities. This deterioration often results in changes in personality and increasing difficulty in thinking and memory over time. Although there is no cure, early detection is crucial as it allows for more effective management and care. Advances in deep learning have significantly improved the accuracy of brain scan analysis for diagnostic purposes. In this study, we utilized the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset consisting of subjects diagnosed with AD, Mild Cognitive Impairment (MCI), and Normal Control (NC). Each participant has either Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET) neuroimaging data, ensuring representation across heterogeneous modalities. The research focuses on comparing gradient-based, edge-based, and segmentation-based data augmentation techniques for early AD detection using neuroimaging and deep learning approaches, particularly 3D Convolutional Neural Networks (3D CNNs). Various augmentation methods were applied, including directional gradient, azimuth gradient direction, numerical gradient, Sobel horizontal edge filter, superpixel oversegmentation, and Canny edge detection. These techniques are evaluated in both binary and multiclass classification tasks involving MRI and PET scans. The results indicate that optimal performance varied depending on the task and modality. For PET-based classification, directional gradient performed the best for AD vs. NC binary classification, achieving an accuracy of 87.24%, while Canny edge detection was most effective for AD vs. MCI binary classification and AD-MCI-NC multiclass classification tasks, achieving accuracies of 72.77% and 59.04%, respectively. For MCI vs. NC, the best result (accuracy = 64.32%) is achieved by combining azimuth gradient direction with Sobel filtering. In contrast, for the MRI-based AD vs. NC classification task, the highest performance is achieved without applying augmentation (balanced accuracy = 60.90%). This research confirms the efficacy of data augmentation methods in the early diagnosis of AD in clinical settings. Full article

Controllable Electrolyte Distribution Electrochemical Machining (abbreviated as CED-ECM) is a novel ECM method for shallow groove (with a depth of about several μm to tens μm) fabrication on precision metal surfaces. Rapid and accurate detection of CED-ECM-processed grooves is essential for quality control and automated production cycles. However, these grooves exhibit minute scale, irregular morphology, and low contrast against the metallic background, while grayscale microscopic imaging provides only single-channel information. To address these challenges, this research proposes a lightweight instance segmentation network based on pseudo-multimodal wavelet network (abbreviated as PMW-YOLO). First, a pseudo-multimodal channel fusion strategy expands single grayscale images into three complementary channels: original grayscale, CLAHE-enhanced grayscale, and multiscale Sobel gradients. This design explicitly injects illumination robustness and edge priors without additional acquisition cost. Second, a Discrete Wavelet Transform-based downsampling module, termed DWTDown, is integrated into the backbone to preserve high-frequency edge details while reducing model parameters and GFLOPs. Ablation studies further investigate the contributions of an Efficient Multiscale Attention module, a boundary-aware mask loss, and data-centric augmentation strategies. Experiments on an in-house CED-ECM dataset validate the effectiveness of PMW-YOLO for automated groove inspection. Full article

To address the challenges of low image quality and difficult feature extraction of weld beads caused by the complex dynamics of the molten pool, intense arc light, and spatter interference during wire arc additive manufacturing (WAAM) of 316L stainless steel, this paper develops a binocular vision-based dynamic molten pool tracking system and conducts image processing and feature analysis. Two high-speed CMOS cameras are employed to capture images of the molten pool and weld bead. Camera calibration is performed to convert pixel coordinates to world coordinates. The denoising performance of five filtering methods, namely mean, Gaussian, median, maximum, and minimum filters, is systematically compared, and the minimum filter is selected for noise reduction. Adaptive threshold binarization, adapthisteq image enhancement, and morphological threshold segmentation are integrated to effectively separate the weld bead from the background. Four edge detection algorithms—Sobel, Robert, Laplacian, and Canny—are compared, and the Canny algorithm combined with Hough transform line fitting is determined to achieve complete and continuous extraction of the weld bead contour. The Intersection over Union (IoU) metric is introduced for image quality screening. When IoU is set to 0.3, the detection accuracy exceeds 90%, effectively eliminating defective images caused by spatter, explosion, trailing, and other disturbances. The proposed method facilitates stable extraction of geometric parameters (e.g., pixel area of the weld bead and height/width of the molten pool), thereby offering a feasible image-processing solution for dynamic molten-pool monitoring and online quality assessment of 316L stainless steel components fabricated by wire arc additive manufacturing. Full article

The development of reliable computer vision systems for poultry slaughterhouses requires robust segmentation methods capable of handling challenging industrial conditions. This study evaluated the structural behavior, computational performance, and consistency of three segmentation pipelines for broiler chicken carcass inspection: (i) an edge-based approach using Canny detection and morphological operations, (ii) a threshold-based method using global binarization and contour extraction, and (iii) a deep learning-based approach for automatic background removal implemented with the rembg library. A dataset of 587 RGB images acquired in commercial slaughterhouses was analyzed. Segmentation performance was assessed using Intersection over Union, Dice coefficient, Overlap Error, Structural Similarity Index, Edge Preservation Index, and Fisher Discriminant Ratio, complemented by qualitative analyses of discordance maps and Sobel edge visualizations. Results showed that overlap-based metrics alone were insufficient, as high IoU and Dice values often concealed important boundary differences. Classical methods exhibited lower computational cost and processing times compatible with real-time applications but presented limitations in contour stability. The deep learning-based approach generated more continuous and structurally coherent boundaries, although at higher computational cost. These findings demonstrate that segmentation methods produce distinct structural representations that can directly affect the reliability of artificial intelligence systems for poultry inspection. Full article

Aging road and concrete infrastructure requires efficient crack inspection methods that can support timely maintenance while reducing manual labor and computational cost. Existing lightweight detectors often improve speed by compressing features, but this may weaken the representation of crack edges and fine textures. This paper proposes GSE-YOLO, an edge-enhanced lightweight crack detection framework based on YOLO11n for resource-efficient infrastructure monitoring. The framework introduces GhostEdgeConv, which combines Ghost-style feature generation, Sobel edge responses, and an edge-gated enhancement mechanism to strengthen slender and low-contrast crack features with limited overhead. SPPELAN and EMA are further incorporated to improve multi-scale contextual aggregation and key-region feature recalibration. Experiments on the Crack-Seg dataset show that GSE-YOLO achieves a mAP of 81.7%, improving the YOLO11n baseline by 2.5 percentage points. Precision and Recall increase by 4.7 and 1.5 percentage points, respectively, while parameters and GFLOPs are reduced by 17.8% and 14.3%, and the inference speed reaches 500 FPS. These results indicate that GSE-YOLO can provide a practical balance between detection accuracy, computational efficiency, and deployability for sustainable infrastructure maintenance. Full article

X-ray testing is a cornerstone nondestructive testing (NDT) technique in the nondestructive testing of welds. To address the challenges posed by minute defects such as cracks and pinholes—characterized by small size, weak features, and a tendency to be confused with noise—this paper proposes a minute defect recognition framework based on sparse representation. (1) Median filtering was selected as the basic denoising method. In combination with image enhancement, the discriminability of weld regions and defect features was improved. (2) A segmented ROI extraction method combining Otsu threshold segmentation and Sobel edge detection was proposed. This method can better adapt to inclined or curved weld images and effectively reduce background interference. (3) A micro-defect recognition method based on sparse representation was proposed. By constructing an SDR and combining dictionary learning with sparse solving models, effective representation and classification of micro-defect regions were achieved. Its effectiveness and engineering application value were verified through actual engineering data, third-party witness tests, and competition results. Full article

UAV-based road traffic state monitoring and analysis have become a hotspot in current research, where road region detection serves as the prerequisite for the aforementioned applications. This paper proposes a UAV-driven edge-based lane-detection system, and a lane line semantic segmentation, modeling and road-region-detection method. Firstly, a lightweight lane line semantic segmentation model LSLNet is presented, where the strip- aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) are used in conjunction to improve feature representation ability under low computational overheads. Furthermore, the segmented lane line mask is quantified into a parametric form and the lane-level road regions are constructed by lane line spatial geometric distribution. Finally, to evaluate the performance of the proposed method, an experiment is conducted using the self-constructed UAV-Laneline3K and UAV-Roadregion200 datasets. The experimental results show that LSLNet achieves 82.73% F1-score and 72.06% mIoU on the lane line semantic segmentation task, which runs at 82 FPS with merely 0.09M parameters and 13.0 GFLOPs. For road region detection, the mIoU and F1-score reach 97.62% and 98.86%, respectively. The results demonstrate that the proposed method enables accurate and robust road region detection in complex road environments with low computational costs. Full article

For high-speed pneumatic seeders, accurate real-time seed detection underpins downstream quality assessments including seed counting, seeding-rate estimation, and uniformity evaluation. Under high-speed operating conditions, seeds exhibit rapid motion, dense distribution, frequent occlusion, and severe motion-blur-induced edge degradation, posing substantial challenges for vision-based detection. This study proposes HSSD-YOLO, an improved detection algorithm built upon YOLOv11, incorporating three modules: a Motion Blur Enhanced Stem module (MBE-Stem) employing learnable Sobel gradient operators for edge feature extraction under motion blur; an Attention-enhanced Deformable Convolutional Network (ADCN) with a Residual Spatial-Channel Attention (RSCA) mechanism for adaptive sampling of irregularly shaped seeds; and an Edge-Guided Adaptive Recalibration Feature Pyramid Network (EGAR-FPN) injecting edge prior information into multi-scale feature fusion. On a self-constructed dataset of indica rice, japonica rice, and wheat seeds, HSSD-YOLO achieves 96.6% mAP@0.5 and 77.4% mAP@0.5–0.95, surpassing YOLOv11n by 2.5 and 5.4 percentage points, respectively, with only 5.2 M parameters. Ablation studies confirm synergistic gains exceeding linear superposition. Under the conditions evaluated, HSSD-YOLO outperformed all compared algorithms, providing the per-frame detection foundation for downstream seeding-quality tasks; empirical validation of those tasks on continuous video and embedded hardware remains outside the present scope. Full article

Container surface damage detection is critical for ensuring the structural integrity and operational safety of intermodal freight transport. However, visual pseudo-textures arising from rust stains, specular reflections, and paint weathering cause frequent false positives, while the scarcity of puncture-type defects (Hole class) leads to missed detections. Existing YOLO-family detectors address neither the frequency-domain characteristics of such pseudo-textures nor the physical priors inherent in genuine structural damage. In this paper, we propose PPFS-YOLO, a physics-prior frequency-spatial fusion framework built upon YOLOv12s. Two lightweight modules are introduced: (1) Frequency-Spatial Fusion (FSF), which applies a learnable spectral mask in the Fourier domain and performs gated fusion with spatial features to suppress pseudo-texture responses; and (2) Edge-Guided Auxiliary Supervision Module (FIM), which encodes Sobel-derived edge priors as a differentiable $L_1$ constraint (${\mathcal{L}}_{\mathrm{phy}}$) to regularize feature learning toward physically plausible damage boundaries. Three pairs of FSF–FIM are inserted into the YOLOv12s neck and head at P3, P4, and P4-head scales. Experiments on a container damage dataset containing 7013 images and three classes (Dent, Hole, Rusty) demonstrate that PPFS-YOLO achieves 64.86% mAP@50, a +12.35 percentage-point improvement over the YOLO12s baseline (SGD, unified optimizer), with only +0.79 M additional parameters (+8.6%) and a modest latency overhead of 2.9 ms (17.2 ms vs. 14.3 ms at $640\times 640$ on an NVIDIA RTX 3090 GPU (NVIDIA Corporation, Santa Clara, CA, USA)). Ablation analysis reveals that ${\mathcal{L}}_{\mathrm{phy}}$ is the critical catalyst: without it, the combined FSF+FIM modules yield only +0.83 pp, whereas the full model achieves +12.10 pp—underscoring the synergy between frequency-domain fusion and physics-prior regularization. Full article

Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP_50–95, mAP₅₀, and mAP₇₅, respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP₅₀ over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture. Full article