Abstract This study proposes a machine vision-based automatic tool setting system for ultrasonic thread root rolling (UTRR), which addresses challenges in tool setting accuracy and the inefficiency of traditional manual or contact-based methods. The system integrates ROI extraction based on YOLOv5, an improved Canny edge detection algorithm, and the Iterative Least-Squares Piecewise Fitting Algorithm (ILS-PFA) to build a robust tool setting framework suitable for complex metallic threads under challenging conditions such as strong reflections and shadows. YOLOv5 is employed for reliable detection of the thread and rolling tool, even under varying lighting conditions. The improved Canny algorithm, augmented with adaptive bilateral filtering, Sobel gradients, and Otsu thresholding, effectively suppresses noise from metallic reflections and enhances edge clarity. The ILS-PFA method reconstructs thread profiles by fitting arc and line segments, while iterative optimization restores data lost due to shadows and reflections. A localization method based on curvature transition is used to precisely locate the thread root feature point, and the system calculates the spatial distance between the tool feature point and this feature point using camera calibration parameters. Experimental results demonstrate that the proposed method achieves micron-level measurement accuracy, with a mean absolute error of approximately 3.2 μm and a maximum relative error below 0.4% across different thread specifications. The tool-setting repeatability exhibits sub-micron stability, with a standard deviation below 0.6 μm. The total execution time of the automatic tool-setting process is approximately 1.78 s, satisfying practical industrial requirements. Error analysis indicates that the overall system error is controlled within 4 μm. These results confirm that the proposed method provides a robust, accurate, and cost-effective solution for automatic tool setting in UTRR applications.

Abstract Belt conveyors are widely utilized in material handling applications because of their high efficiency and substantial capacity. However, belt conveyor deviation typically leads to transmission system failures, which in turn impact production efficiency and may even cause serious safety incidents. Traditional sensor-based detection methods are sensitive to environmental noise, lighting changes, and material interference. Deep learning approaches face challenges such as high computational complexity, limited edge localization accuracy, and baseline drift due to camera position shifts. This paper proposes a deviation detection method that integrates deep learning with geometric constraints. The DC-YOLO-seg model, based on enhanced YOLOv11-seg, is combined with deformable convnets v3 (DCNv3) and coordinate attention (CA) mechanisms for high-precision instance segmentation of conveyor belts and rollers. Subsequently, the belt centerline was extracted using the Canny edge detection algorithm and random sample consensus (RANSAC) fitting method. The drive centerline was estimated based on the alignment of the roller centers, thereby quantifying the offset distance. This approach effectively reduces dependency on camera position and minimizes environmental interference. Experimental results on a self-constructed dataset demonstrate that DC-YOLO-seg achieves a Mask (mAP50-95) of 0.948, representing a 2.5% improvement over baseline models. The deviation detection error is generally maintained within 5 mm. This research provides a robust solution for intelligent operation and maintenance, establishing a foundation for cross-scenario generalization and real-time deployment.

Blind super-resolution (SR) aims to reconstruct high-resolution images from low-quality inputs under unknown degradation conditions. While numerous blind SR methods have been proposed in recent years, they still face critical limitations. Most approaches perform well under specific degradation patterns but struggle with complex scenarios involving multiple degradation factors and varying noise levels. This often leads to loss of structural integrity and fine details, resulting in suboptimal restoration quality. Furthermore, existing methods typically rely on convolutional neural networks (CNNs) with limited receptive fields, which hinders effective cross-domain information integration. Their inability to capture long-range dependencies compromises the reconstruction of global structures. In edge detection, conventional techniques frequently produce inaccurate or false edges, further degrading the quality of cross-domain integration and image restoration. To address these challenges, we propose Structural Integrity and Texture Fidelity Transformer (SITFFormer), a novel transformer-based framework for blind single-image SR. Our approach incorporates the Canny edge detection algorithm to accurately preserve true edges and suppress noise-induced artifacts, enhancing edge localization in complex and noisy environments. We also introduce the Cross-Domain Structure-Texture-Aware Network (CDSTNet), designed to integrate intra-domain and cross-domain features for comprehensive structure preservation and texture recovery. CDSTNet comprises two key modules: Cross-Domain Integration (CDI) that fuses intra- and cross-domain features to retain structural and textural details. Cross-Domain Learnable Attention (CDLA) that explores global dependencies, adaptively refines feature similarity, and filters out redundant non-local information. Both modules are equipped with a Cross-Attention Mechanism (CAM) to facilitate effective interaction and complementarity between domains, enhancing reconstruction fidelity. Extensive experiments on synthetic, noisy, and real-world datasets demonstrate that SITFFormer surpasses state-of-the-art methods in quantitative performance and visual quality, particularly in preserving structural integrity and recovering fine textures.

Gear accuracy directly affects gear quality and the performance of mechanical transmission. To address the inefficiency of contact measurement for medium-module spur gears and the high cost of precision measuring instruments, this paper proposes a vision-based non-contact measurement method for medium-module spur gears. The method establishes an optical imaging and vision measurement framework tailored to medium-module spur gears, and applies image preprocessing to enhance edge features. Subsequently, gear contour information is extracted using the least-squares method, and precise subpixel localization and deviation measurement of tooth profiles are achieved by combining the involute gear geometry with an improved subpixel edge detection algorithm based on the Bertrand gray surface model. Experiments were conducted on four spur gears with a module of 5 mm. The results show that the calibrated pixel scale of the system is 36.1869 μm/pixel, and the maximum measurement error for the addendum circle radius is 0.342 mm. Furthermore, the tooth profile deviation curves obtained by the vision system closely matched those from a gear measuring center, and the measured deviations exhibit good consistency with the reference measurements. These findings validate the practicality and reliability of the proposed method for geometric parameter extraction and individual deviation measurement of medium-module spur gears.

The Booth multiplier provides high-performance signed multiplication by encoding and decreasing partial products (PPs) generated using the radix-4 Booth algorithm. Although the radix-8 produces fewer PPs than the radix-4 and needs fewer adders to accumulate PPs, it is not fast because the odd multiples of the multiplicand are generated in a complex unit, and attaining a high performance is challenging. This work alleviates this issue using approximate designs. An approximate 4:2 compressor is proposed in which the inputs are encoded by the generation and propagation method for the reduction of faulty rows in the truth table. The compressor, radix-8 Booth encoder, and PP generation (PPG) are used to attain a signed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times 16$</tex-math> </inline-formula>-bit, approximate multiplier, and synthesized targeting a 90 nm complementary metal oxide semiconductor (CMOS) technology. The multiplier is efficiently implemented on field programmable gate arrays (FPGAs) to perform the Sobel operator for edge detection. The occupied area, dynamic power dissipation, and power-delay-product (PDP)<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $</tex-math> </inline-formula> mean relative error distance (MRED) of the presented multiplier are superior to the lookup table (LUT)-based multipliers of an FPGA. The Sobel edge detection algorithm implemented on the FPGA detects 99.15% of edges with 33.33% energy savings, while the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) are 0.88 and 32.92dB, respectively.

Optimised Canny edge detection algorithm for medical image feature mapping and extraction

Abstract This study presents a real-time dynamic reconfiguration strategy for photovoltaic (PV) arrays under partial shading conditions, leveraging a metaheuristic Firebug Swarm Optimization (FSO) algorithm integrated with autonomous drone-based image analysis. High-resolution images of the PV array are captured throughout the day using an autonomous drone and subsequently processed using an Otsu–Canny edge detection algorithm to accurately remove noise, extract shading features, and classify the shading into distinct patterns. This processed shading information drives a Total Cross-Tied (TCT) switching matrix, which dynamically rewires a 5 × 5 PV array in real time without physically moving the modules. The proposed FSO-based reconfiguration framework optimizes both module-level connections and array power output, demonstrating a significant reduction in mismatch losses. Performance is rigorously evaluated across three distinct shading scenarios (10:00 AM, 1:00 PM, and 4:00 PM). Detailed analysis of Case 1 (Right-Bottom Diagonal shading) highlights the method’s effectiveness: the system with FSO-driven reconfiguration experimentally achieved 8265.68 W (with an efficiency of 15.2%), which is a substantial improvement over the 7599.24 W achieved without reconfiguration. Simulation results further validated this performance, reaching 9278.62 W. The proposed approach consistently outperforms conventional techniques like Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) in terms of power yield, enhanced fill factor, and overall efficiency across all tested cases. The successful integration of drone-assisted shading detection, advanced image processing, and FSO-driven dynamic reconfiguration provides a robust, scalable, and efficient solution for maximizing PV energy yield under diverse and time-varying partial shading conditions, offering a clear implementation pathway for enhancing modern PV installations.

Point Cloud Segmentation Method for Rock Pile Particle Size Analysis Based on Edge Detection and Region Growing

ABSTRACT To enhance cotton/viscose fiber recognition accuracy, we propose a data classification-based digital analysis method. A set of image preprocessing algorithm procedures has been developed to perform grayscale, denoising, and binarization on the image. In addition, the fiber cross-sectional image is marked, and the outer contour image of the fiber cross-section is obtained by using the edge detection algorithm, and finally the feature parameters are extracted. By training and analyzing the extracted fiber cross-section characteristic parameters, three recognition algorithms including K-nearest neighbor, backpropagation neural network, and random forest–decision tree are used to identify the fiber category. The experimental results show that the recognition rate of the random forest–decision tree algorithm is the highest, and the recognition accuracy is up to 97.5 %.

Abstract This paper proposes a novel Machine Vision (MV) - based technique to assess surface roughness (Ra) as an Online Diagnostic and Monitoring system tool. Such a tool helps reduce operational downtime and minimise human intervention. The proposed method involves image acquisition in a controlled environment, detection of surface features through a parameter called “Edge frequency” using the Edge Detection Algorithms (EDAs), and developing a correlation between the estimated Edge frequency and the experimentally measured Ra. Several well-known EDAs are evaluated on different samples, including emery papers with various grit values and turbomachinery blades. The superiority of LoG (Laplacian of Gaussian)-based EDA in terms of its resilience to noise and computational benefit is demonstrated. The Edge frequency-Ra correlation is subsequently used to predict the Ra value of different sets to demonstrate prediction accuracy. Compared to contact-based measurements, the predictions on the emery samples are within 4.8%, while those on the blade samples are within 5.9%. Finally, the established correlation is used for the online diagnosis of a Legacy axial compressor under cleaned and fouled conditions. The predicted Ra values for the samples and the trends agree with the historical data reported by the Original Equipment Manufacturers (OEMs), demonstrating its ability as an On-board Diagnostic (OBD) tool.

Vehicle license plate detection is an essential component in modern parking management, particularly in institutional areas like PLN Mabar, which necessitate fast and accurate identification systems. This study focuses on applying the Canny Edge Detection method to accurately identify the edges of vehicle license plates under specific environmental settings, specifically lighting conditions of 100–115 lux and a camera height of 40 cm, evaluated across various threshold levels. Widely regarded as an optimal edge detection algorithm, the Canny Edge method offers significant advantages, including high-precision edge detection, robust noise interference minimization, and the generation of clear object boundaries. The research findings demonstrate that this method delivers excellent performance for vehicle detection in parking facilities when operating under controlled lighting and camera parameters. Specifically, the test results reveal that within a threshold range of 50 to 500, the system achieves a flawless 100% detection accuracy. This highlights the method's effectiveness in capturing crucial object edges under the tested conditions. Conversely, increasing the threshold beyond 500 leads to a gradual decline in system accuracy, dropping to 20% at a threshold of 900–1000. This decline indicates that excessively high threshold values cause the system to discard vital contours necessary for accurate detection. Ultimately, the system successfully detects license plate edges with a high success rate and stable processing times, proving its viability for practical implementation within the vehicle identification system at the PLN Mabar parking area.

Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization methods, such as edge detection algorithms and active contour models, often fail under these conditions, producing fragmented and unreliable fringe contours. This paper presents a novel skeletonization procedure that simultaneously addresses three fundamental challenges: (1) topology preservation-by representing the fringe family within a physics-informed, finite-dimensional parametric subspace (e.g., Fourier-based contours), ensuring global smoothness, connectivity, and correct nesting of each fringe; (2) extreme noise robustness-through a robust strip integration functional that replaces noisy point sampling with Gaussian-weighted intensity averaging across a narrow strip, effectively suppressing speckle while yielding a smooth objective function suitable for gradient-based optimization; and (3) sub-pixel accuracy without phase extraction-leveraging continuous bicubic interpolation within a recursive quasi-optimization framework that exploits fringe similarity for precise and stable contour localization. The method's performance is quantitatively validated on synthetic interferograms with controlled noise, demonstrating significantly lower error compared to baseline techniques. Practical utility is confirmed by successful processing of a real interferogram of a bent plate containing over 100 fringes, enabling precise displacement field reconstruction that closely matches independent theoretical modeling. The proposed procedure provides a reliable tool for processing challenging interferograms where traditional methods fail to deliver satisfactory results.

The load-bearing capacity analysis of pre-stressed concrete in bridge engineering is a core technology for structural safety evaluation. It has long faced challenges in insufficient detection accuracy under complex stress environments and low efficiency in multi-source data fusion. Traditional analysis methods rely on a single mechanical model or empirical experience, making it difficult to accurately capture the nonlinear relationship between crack development and load-bearing capacity degradation. Therefore, this study proposes a prestressed concrete load-bearing capacity analysis model based on a dual-threshold edge detection algorithm. Experimental results show that the accuracy of the improved edge detection algorithm reaches a maximum of 89.5% after iteration, with the misdetection rate of bridge cracks under various noise influences being as high as 9%. Evaluation of the fusion analysis model shows that the Mean Square Error (MSE) of its load-bearing capacity is only 0.015 kN·m2 , and the coefficient of determination R2 is 0.98. These results indicate that the proposed prestressed concrete load-bearing capacity analysis model can effectively improve the prediction accuracy of load-bearing capacity under complex stress environments and accurately capture the nonlinear relationship between crack development and loadbearing capacity degradation. Compared with existing research, the core contributions of this study are reflected in three aspects: 1. A collaborative analysis framework for crack characteristics and section loss was constructed, quantifying the coupling influence mechanism of the two on bearing capacity and breaking through the limitations of traditional single-factor analysis; 2. A prestressed concrete bearing capacity analysis model was proposed. Through algorithms, the core characteristics of crack-section loss were precisely screened, and the problem of dynamic bearing capacity prediction under small samples was solved, filling the technical gap of nonlinear mapping in complex stress environments; 3. The experiment verified the quantitative correlation between crack size and steel bar damage (for every 0.1mm increase in crack width, the steel bar corrosion rate increases by approximately 15%), providing an operational quantitative method for inferring internal structural damage from surface cracks. This study provides a new technical approach for bridge structural safety assessment and contributes to the development of intelligent monitoring and full-life-cycle maintenance technologies for prestressed concrete structures. Keywords: Bridge engineering, LSTM, Otsu, PSO, Crack characteristics, GAN.

Under the increasing urgency for global water resource exploration, conventional flow velocity measurement instruments are inadequate to meet the demands of long‐term monitoring in complex environments. This article proposes a self‐powered aquatic flow velocity sensor based on triboelectric nanogenerator (TENG) technology, enabling the detection of water flow velocities. The signal processing circuit designed in this article can step down the high voltage signals generated by the TENG, exceeding 400 V, to approximately 1.7 V, while maintaining the ability to accurately reflect variations in flow velocity. In addressing the issue of flow velocity signal jitter in complex aquatic environments, data processing was performed using a Time‐Frequency Cooperative Adaptive Edge Detection Algorithm. Post‐processing results showed a deviation of 0 Hz in the primary frequency component compared to the original signal, with a spectral root mean square error of 0.058, indicating accurate reconstruction of flow velocity information. The sensor's effective measurement range spans from 0.1 to 2.4 m/s, adequately fulfilling the flow velocity monitoring requirements across a variety of common aquatic environments. This article offers a low‐cost, self‐powered innovative solution for dynamic water resource monitoring, demonstrating broad application prospects in hydraulic engineering, hydrological monitoring, and related fields.

This paper presents an adaptive method for extracting fabric pattern templates, focusing on efficiently and accurately digitizing textural features in traditional fabric images. Using Segment Anything Model 2 (SAM2) automatic mask generation as the core, this study precisely segments color blocks in fabric images, providing high-quality data for further processing. The method employs a multistep strategy. First, color quantization and bilateral filtering reduce image complexity, remove noise, and enhance edges. Second, advanced edge detection algorithms identify prominent edges to assist SAM2 segmentation, ensuring accuracy and reliability. Finally, masks generated by SAM2 are classified and merged based on their covered colors in the original images, producing clear pattern templates. This method is validated with numerous real fabric images, and it shows strong adaptability and efficiency in extracting color templates. It provides robust support for digital preservation of traditional fabric patterns and opens up opportunities for innovative applications and heritage development, marking a significant advancement in this field.

Intrusion and trespasser detection on railway tracks is a crucial safety measure to prevent accidents and maintain operational reliability. This study proposes a hybrid vision-based approach that integrates YOLOv8n, a lightweight real-time object detection model, with the Canny edge detection algorithm to identify and classify unauthorized objects and individuals on railway tracks. In this context, intrusions refer to inanimate objects such as rocks, fallen trees, or construction materials obstructing the tracks, whereas trespassers refer to humans or other living beings engaging in unauthorized activities near or on the railway line. YOLOv8n is employed as a single-stage detector to localize and classify objects, while Canny edge detection is applied to enhance object contours and improve shape-based differentiation between intrusion and trespasser categories. Experimental results show an average accuracy of 52.37%, indicating moderate detection performance. Although the accuracy remains limited, the findings demonstrate the potential of combining deep learning and traditional image processing techniques to develop an automated monitoring system that supports railway safety and surveillance applications. Further optimization of the dataset, model tuning, and feature enhancement are recommended to improve detection performance.

Unmanned aerial vehicles are extensively utilized for image acquisition in a cheap, fast, and effective way. In this study, an automatic crack detection method with non-crack object removal and deep learning-based approaches are developed and tested on images captured by unmanned aerial vehicle. The motivation of this study is to detect either a crack exists or not in the asphalt-runway. The novelty of this study lies in integrating a non-crack artifact removal process with six classical edge detectors and comparing the resulting performance with four lightweight CNN models on the same UAV-acquired runway image dataset, enabling a unified evaluation of classical and learning-based approaches. For deep learning-based approach, four lightweight CNN models, namely GoogleNet, SqueezeNet, MobileNetv2, and ShuffleNet, are trained and the best accuracy of %87.9 is obtained whenever GoogleNet model is used. For the non-crack object removal approach, exclusion of non-crack objects from the images is the first step, where crack-detection which makes use of edge-detection techniques is the latter. In the study, Sobel, Prewitt, Canny, Laplacian of Gaussian, Roberts and Zero Cross edge detection algorithms are examined and their success rates in detecting cracks are comparatively presented. With sensitivity=0.981, specificity=0.744, accuracy=0.917, precision=0.912 and F-score=0.945 values Canny algorithm performs significantly better than others in detecting the cracks. This study provides enough evidence for the practicability of automated crack detection on unprocessed digital photographs by the results of the study conducted on asphalt runway.

Accurate and timely classification of white blood cells (WBCs) is crucial for diagnosing a myriad of hematological disorders, including leukemia. While deep learning models, particularly Convolutional Neural Networks (CNNs), have shown promise in automating this task from microscopic blood smear images, their performance can be hindered by complex backgrounds and intra-class variations. This paper proposes a novel segmentation-enhanced classification framework that synergistically combines classical image processing with deep learning. Our approach first employs a fixed-parameter Canny edge detection and contour-based algorithm to segment the WBC foreground. Subsequently, a learnable blending layer intelligently fuses the segmented foreground with the original image, allowing the downstream CNN to leverage both focused object information and contextual cues. We meticulously document our experimental journey, including initial attempts to train Canny parameters which proved unstable. The proposed model, featuring fixed segmentation and a learnable blending factor ( α ), was evaluated on a public blood cell image dataset for cancer detection. It achieved a test accuracy of 99.93%, outperforming a baseline CNN (99.07%). The blending factor α converged to approximately 0.63, indicating an optimal balance between the segmented and original image content. Furthermore, the segmentation-enhanced model demonstrated faster inference times. This work underscores the potential of hybrid approaches, particularly the utility of a learnable blending mechanism to effectively integrate classical segmentation outputs into neural networks for improved classification.

Cultural heritage represents humanity's invaluable treasure, yet existing image restoration methods suffer from inadequate edge feature extraction capabilities and aesthetic evaluation approaches that cannot be co-trained with emotional factors. Consequently, this research proposes an image restoration method based on an enhanced You Only Look Once version 5 small (YOLOv5s) and multi-scale residual fusion gated convolutions, integrating an aesthetic evaluation model that incorporates emotional elements. This approach employs the enhanced YOLOv5s for image extraction, utilizing the Canny operator as the edge detection algorithm. It incorporates multi-scale residual blocks and gated convolutional networks within the generative adversarial network, employing pixel reconstruction, perceptual, and style loss as the joint loss function. The research inserts emotion label extraction and emotion fusion modules into the residual network. Experiments demonstrate that the enhanced YOLOv5s achieves a maximum image extraction accuracy of 95.3%, surpassing both YOLOv5s and YOLOv8 by 12.5% and 0.3% respectively, whilst converging significantly faster. The image restoration model exhibits higher structural similarity indices, with restored images most closely approximating reality at a maximum value of 94.5%. Removing multi-scale residuals substantially impacts model performance. The aesthetic evaluation model achieves a maximum Spearman's correlation coefficient of 0.792 with the lowest computational complexity. Consequently, the proposed methodology effectively enhances image restoration capabilities and aesthetic evaluation quality, thereby facilitating the wider dissemination of cultural heritage.

Shearer is an important mechanical equipment in coal mining, and wear is the main cause of failure of its picks. The cutting force model for pick-type picks cutting coal seams with coal-rock interfaces was established. A coal-rock cutting test bench was used to carry out single-cutting pick coal-rock cutting tests under various working conditions. Image processing technology and the Kirsch edge detection algorithm were adopted to obtain the geometric data of the wear amount of the shearer picks under impact wear conditions. This study proposes the wear rate of the pick body thickness and the wear length of the pick body as evaluation indices for the wear degree of the cutting picks, collects the mean values of the cut loads under different wear degrees of the cutting picks, and analyzes the effects of factors such as the drum rotation speed, cutting thickness, impact angle, tilt angle, and the position of the coal-rock interface on the damage characteristics and wear amount of the picks. The results show that Kirsch operator exhibited good edge positioning capability and excellent image processing effect, with an accuracy rate as high as 96.5%. When the rock stratum is at position A, the wear amount of the cutting pick is the smallest. When the impact angle is 45°, the inclination angle is 5°, the drum rotation speed is 60–90 r/min, and the cutting thickness is 10–15 mm, the wear amount of the pick can be reduced.