Effect of accelerating voltages on indexing of SEM-EBSD via pattern matching.

This study develops an intelligent processing system that integrates monitoring, identification, and assessment of lodged wheat to address yield loss during mechanical harvesting caused by lodging in precision agriculture. This system provides decision support for automatic header adjustment and disaster assessment. The system uses Visual C++ as the integrated development platform and comprehensively applies machine vision technology. Features of the lodged straw layer were enhanced through color difference analysis, linear grayscale transformation, and grayscale inversion. Image enhancement was achieved using median filtering. Precise segmentation of the straw layer and wheat ear layer was realized using mathematical morphology methods (erosion and dilation). Combining Roberts operator edge detection and Hough Transform line detection, lodging boundaries were extracted, and the lodging height difference was calculated. A fully functional software system was successfully developed. The system accurately identifies lodging areas, with detected lodging boundaries showing high conformity to actual conditions, and calculated height difference parameters prove reliable. The system achieved automated processing from image input, providing an effective technical solution for reducing harvest losses in precision agriculture.

Accurate and efficient lane detection is required for the effective operation of autonomous vehicles and advanced driver-assistance systems need it for their functioning. Traditional methods frequently use computationally demanding trigonometric operations, which are particularly challenging during line detection using the Hough Transform. One of the focused areas of this study is a real-time lane detection framework based on CORDIC technology. The CORDIC system operates as an iterative process that performs fundamental mathematical functions to enable its implementation on embedded platforms, whereas the matrix Mactor usage of CORDIC iterative methods presents an alternative to regular sine and cosine computations. The proposed pipeline implementation combines region-of-interest masking with Canny edge detection, modified Hough Transform, and CORDIC methods to detect multiple straight lane lines. The CORDIC implements the polar line equation using an iterative rotation method, thereby minimizing the computational requirements. The method can accurately extract multiple lane lines while significantly increasing the processing speed, as shown in the experimental results for both clear and rain-blurred highway images. The CORDIC-enhanced method shows advantages over standard algorithms through timing benchmarks, accumulator space visualizations, and performance metrics, which display the complete results of in-depth comparisons. This study demonstrates how hardware-oriented computation combined with algorithmic optimization enables real-time automotive applications and intelligent transportation systems to scale while achieving a correct rate of 98.72%.

An FPGA implementation of the Hough Transform tracking algorithm for the Phase II upgrade of ATLAS

This study presents the development and field evaluation of an autonomous four-wheel drive (4WD) agricultural prototype equipped with a cost-effective image-based navigation system. While high-precision positioning typically relies on expensive RTK-GNSS systems, this research explores the operational limits of handcrafted feature extraction methods, specifically Canny Edge Detection and Probabilistic Hough Transform, on a resource-constrained Raspberry Pi 4B platform. The methodology includes structured field trials in a 30-metre corn field, with 10 successful autonomous runs conducted under three different lighting scenarios: sunny, cloudy, and twilight. Navigation accuracy was measured using Mean Cross Tracking Error (MCTE) with measurements recorded at 3-metre intervals. Results show that the system achieved its highest stability under cloudy (diffuse) conditions, with a minimum MCTE of 6.2 cm and 95% accuracy. A performance decrease was observed in twilight conditions (MCTE: 12.5 cm) due to a decrease in the signal-to-noise ratio (SNR) and in sunny conditions (MCTE: 8.0 cm) due to shadow-induced interference. The findings indicate that four-wheel drive platforms combined with optimised vision pipelines offer a viable, low-cost alternative for small-scale agricultural automation, provided that environmental lighting variability is addressed.

Generating three-dimensional reinforcement models from construction drawings is resource-intensive and requires extensive domain expertise. While building information modeling has advanced automation for architectural elements, reinforcement drawings remain largely overlooked despite their critical role in construction. This study presents an automated framework utilizing an improved Hough transform algorithm to detect and extract reinforcement information directly from construction drawings. The enhancement integrates spatial positioning and inter-element distance features into the classical algorithm, significantly improving segmentation precision and enabling accurate identification of rebar lines at structurally critical locations. A standards-driven annotation matching mechanism further enhances interpretability by leveraging construction norms to decode textual labels, symbols, and graphical cues. Experimental evaluations across diverse datasets consistently achieve accuracy rates exceeding 96%, demonstrating the method's robustness and practical applicability.

Autonomous driving (AD) systems remain vulnerable to rare, ambiguous, and out-of-label (OOL) hazards that are insufficiently represented in conventional training datasets. This work investigates perception robustness under such conditions by using the Challenge of Out-Of-Label (COOOL) benchmark dataset, which consists of 200 dashcam video sequences annotated with both common and uncommon traffic hazards. We analyze that the behavior of widely used methods in the perception of components and present a multimodal pipeline in which we integrate YOLO11x for object detection, Hough Transform for lane estimation, and GPT-4o for scene description, and for temporal modeling, we use Long Short-Term Memory (LSTM) networks. On the COOOL benchmark, YOLO11x achieves an mAP@0.5 of 54.1% on the common object categories, whereas the detection of rare and OFL hazards remains challenging, with a recall of 72.6%. Incorporating temporal risk modeling improves hazard recall to 71.8%, indicating a modest but consistent gain in recognizing uncommon events. Hough Transform shows the stable behavior in standard conditions for lane estimation, with a mean lateral deviation of 8.9 pixels in daylight scenes and 13.4 pixels under low-light conditions. The temporal anomaly detection module attains an AUROC of 0.65, reflecting the limitation but meaningful discrimination between nominal and anomalous driving situations. For interpretability, the GPT-4o scene description module generates context-aware textual explanations with an object coverage score of 0.72 and a factual consistency rate of 78%, as assessed through manual inspection. The end-to-end pipeline operates at approximately 10–12 frames per second on a single GPU, supporting near-real-time analysis and optimization. Our results confirm that state-of-the-art perception models struggle with OOL hazards and that multimodal vision–language–temporal integration provides incremental improvements in robustness and interpretability when evaluated under the standardized out-of-distribution conditions.

This paper proposes a hybrid medical image registration framework aimed at improving the alignment accuracy of multimodal brain MRI images. The proposed approach integrates the geometric robustness of fiducial markers with the multi-resolution frequency analysis capability of the Daubechies wavelet transform (db2). Initially, three artificial circular fiducial markers are placed at stable anatomical landmarks and automatically detected using the Circular Hough Transform with radii ranging from 6 to 20 pixels, enabling reliable estimation of an initial affine transformation and reducing gross alignment errors. Subsequently, a two-dimensional Discrete Wavelet Transform (DWT) is applied to the reference and moving images, where the low-frequency (LL) sub-bands are exploited to perform fine registration using Mutual Information (MI) as the similarity metric. This frequency-domain refinement enhances robustness against noise and intensity variations. Experimental evaluations on healthy and tumor-affected brain MRI datasets demonstrate that the proposed hybrid framework outperforms conventional intensity-based methods relying on MSE, SSIM, and PSNR, particularly in challenging pathological scenarios.

Frequency-hopping (FH) communication is widely employed in modern wireless communication systems due to its strong resistance to interference. Accurate detection of FH signals is therefore essential for effective spectrum monitoring and reliable communication in complex electromagnetic environments. However, real-world electromagnetic environments are highly complex and dynamic, with substantial noise and multiple interfering signals coexisting. These conditions pose significant challenges to frequency-hopping signal detection, particularly in terms of low signal-to-noise ratios and co-channel interference. To address these challenges, this paper proposes a frequency-hopping signal detection method based on time–frequency cancellation and the Hough transform. The received signals are first preprocessed using time–frequency cancellation and singular value decomposition to suppress noise and fixed-frequency interference. Subsequently, the time–frequency characteristics of the preprocessed signals are extracted, and the time–frequency cancellation ratio is computed to perform an initial determination of the presence of frequency-hopping signals. To further reduce false detections caused by multiple interference sources, the Hough transform is applied to analyze the time–frequency spectrum in greater detail. By jointly exploiting the geometric and statistical characteristics of the signals, accurate detection of frequency-hopping signals is achieved. Experimental results demonstrate that the proposed method enables precise detection of frequency-hopping signals under challenging electromagnetic conditions.

Facial emotional expressions are viewed as the most descriptive way to understand the human’s state of temperament duringconfronting communication. In this work, numerous statistical approaches have been applied to human eye pupils with staticimages of the Chicago face dataset (C.F.D.) to analyze and classify the categories for emotions: Happy, Fear, Anger, andNeutral. This study aims to develop the specific architecture for the image processing domain after applying different enhancementtechniques to the human eye pupil for analysis & recognition of facial expressions. This work is divided into threephases. In the first phase, data preprocessing is performed to prepare according to the work requirement. The colour imagesare converted into negative by applying the pixel intensity controlled mechanism. The second phase defines the boundary tocompute the feature using the Circular Hough Transform algorithm. Lastly, statistical approaches are applied to extractedfeatures to corporate the central point of the pupil. This corporation of the main point presents the effects of emotions. Whilecomparing people of different Age groups, it is concluded that pupils constricted on Anger to varying levels in other agegroups. Suppose further it is discussed about the cross-cultural and gender-wise comparison. In that case, Happy Emotionaffects most and results towards dilated pupils like Anger emotion affects most on, constricting the pupil size.

超级陶粲装置(STCF)是拟建的一台高亮度、对称结构的正负电子对撞机。为应对STCF实验面对的更高亮度和事例率所带来的实时数据处理性能瓶颈，本研究提出了一种基于霍夫变换的针对低横动量粒子的快速径迹识别方法。本研究在传统共形–霍夫变换框架基础上，通过矩阵化建模实现参数空间投票过程的向量化与并行化；此外，针对霍夫累加器高度稀疏的统计特性，引入基于稀疏映射结构的参数空间表示方式，消除了冗余计算与无效访存开销。实验结果表明，该算法在保持高信号留存率的同时，显著提升了计算吞吐量并大幅降低了内存资源占用，为 STCF高阶触发（HLT）系统在实时环境下高效筛选低横动量物理信号提供了关键的算法支撑与可行性验证。

Abstract Iris recognition is one of the most reliable biometric identification technique due to the uniqueness and stability of its patterns. Deep learning techniques have since emerged as a powerful approach for developing more accurate and robust iris recognition systems. The real-time implementation of deep architectures for iris recognition presents notable challenges, primarily due to the substantial computational and memory demands. In this paper, we present a novel lightweight deep convolutional neural network architecture to effectively address the trade-off between classification accuracy and computation complexity. The pre-processing pipeline employed in the work is aimed at accurately localizing and segmenting the iris image. The pre-processing pipeline comprises of Circular Hough Transform (CHT) for precise iris localization, occlusion removal for handling eyelids and eyelashes, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) for photometric enhancement. In the proposed deep architecture, the depthwise convolutions efficiently extract spatial features from each input channel independently, significantly reducing computational cost, while pointwise convolutions enable channel-wise information fusion to learn discriminative and compact feature representations. The traditional softmax layer is replaced with an SVM classifier using a Radial Basis Function (RBF) kernel, which enhances non-linear decision boundary learning and generalization capability through the max-margin principle. The proposed model has outperformed state-of-the-art pretrained models with a recognition accuracy of 99.3% and equal error rate (EER) of 0.3% on a multi-source benchmark iris dataset (CASIA and MMU1) and demonstrates strong cross-sensor interoperability. The proposed framework offers a promising solution for real-time iris recognition in applications with limited computational resources.

With the rapid development of the new energy vehicle industry, the quality control of battery pack glue application processes has become a critical factor in ensuring the sealing, insulation, and structural stability of the battery. However, existing detection methods face numerous challenges in complex industrial environments, such as metal reflections, interference from heating film grids, inconsistent orientations of glue strips, and the difficulty of accurately segmenting elongated targets, leading to insufficient precision and robustness in glue dimension measurement and glue break detection. To address these challenges, this paper proposes a battery pack glue application detection method that integrates the YOLOv11 deep learning model with pixel-level geometric analysis. The method first uses YOLOv11 to precisely extract the glue region and identify and block the heating film interference area. Glue strips orientation correction and image normalization are performed through adaptive binarization and Hough transformation. Next, high-precision pixel-level measurement of glue strip width and length is achieved by combining connected component analysis and multi-line statistical strategies. Finally, glue break and wire drawing defects are reliably detected based on image slicing and pixel ratio analysis. Experimental results show that the average measurement errors in glue strip width and length are only 1.5% and 2.3%, respectively, with a 100% accuracy rate in glue break detection, significantly outperforming traditional vision methods and mainstream instance segmentation models. Ablation experiments further validate the effectiveness and synergy of the modules. This study provides a high-precision and robust automated detection solution for glue application processes in complex industrial scenarios, with significant engineering application value.

The growing demand for efficient hydrogen storage solutions highlights the need for reliable and safe composite overwrapped pressure vessels (COPVs). This study investigates the application of an inline monitoring system combining laser-based measurements and photogrammetric line photography to assess COPV quality during fabrication, including quantitative evaluation of liner concentricity and high-resolution line scanning of the composite surface to detect and measure fiber orientations. Fiber detection and angle measurement using the Hough Transform provide detailed assessment of local winding orientation, while global Fourier Transform analysis supports comparative evaluation across vessels or segments, allowing identification of dominant fiber directions and detection of micro-scale deviations. The integrated approach enables early detection of geometric inconsistencies and localized winding irregularities, providing robust performance-based criteria for accept-reject decisions, while filtering out minor noise and ensuring reliable quantitative evaluation. This framework enhances inline quality control, optimizes material usage, and supports the safe deployment of COPVs in hydrogen storage systems, contributing to efficient and reliable energy storage solutions.

Abstract - Lane detection is one of the most fundamental components of intelligent transportation systems, particularly in autonomous vehicles and modern Advanced Driver Assistance Systems (ADAS). Accurate lane perception enables safe navigation, stable lane keeping, and informed decision-making. Traditional lane detection approaches—such as Canny edge detection, Hough Transform, and color-based thresholding—show reasonable performance in controlled environments but fail under challenging real-world conditions involving low visibility, shadows, faded lane markings, and abrupt illumination changes. With the rise of deep learning, particularly Convolutional Neural Networks (CNNs), models have gained the ability to learn robust lane features directly from data. However, real-world driving requires more than lane detection; it also demands an understanding of drivable areas and the presence of objects such as vehicles or pedestrians. This paper presents a comprehensive review of classical and modern lane detection techniques, with a focus on multi-task deep learning architectures, such as YOLOP (You Only Look Once for Panoptic Driving Perception). We also implement YOLOP on real-world Indian road videos and enhance its performance on nighttime scenes using custom brightness, contrast, and gamma preprocessing. The integration of night enhancement improved lane IoU from 0.72 to 0.84 and pixel accuracy from 0.88 to 0.93. The review highlights major advancements, limitations, research gaps, and future opportunities in machine-learning-based lane detection. The findings emphasize that multi-task learning, domain adaptation, and lightweight models are essential steps toward practical and reliable autonomous vehicle perception systems. Keywords— Lane detection, YOLOP, multitask learning, CNN, semantic segmentation, object detection, night enhancement.

The Paton Welding Journal, 2025, №11. International Scientific-Technical and Production Journal «The Paton Welding Journal» «The Paton Welding Journal» has been published monthly since 2000 in English, ISSN 0957-798X. «The Paton Welding Journal» is a cover-to-cover English translation of the «Avtomaticheskaya Svarka» (Automatic Welding) journal. The «Avtomaticheskaya Svarka» journal has been published monthly since 1948 in Russian, ISSN 005-111X.

PHoRNet: Bell-mouth deformation detection method of cone forgings based on 6D probability voting in Hough space point cloud registration method

ABSTRACT Background Lineaments can be expressed as linear features which are notably brighter or darker than background (anomaly-type) and suddenly changed in brightness (edge-type) in the remote sensing (RS) and digital elevation model (DEM) images. A new method is proposed to extract both types of lineaments from RS and DEM images based on directional continuous wavelet transform (CWT). The method consists of three steps: (i) determination of omni-directional CWT coefficient concerned with image gradient magnitude and omni-direction image reflecting image gradient direction using multi-directional CWT coefficients, (ii) extraction of image features such as extrema and edges using CWT modulus maxima line and (iii) detection of lineaments through segmentation and linkage of image features and linearization of image feature segments. The omni-directional CWT and omni-direction image determined from multi-directional CWT coefficients are associated with image gradient to be applied to image feature extraction, segmentation and linkage. The positive and negative lineaments can also be detected by the method. The proposed method is tested using a simple example image and compared with the Hough transform (HT) method and applied to real RS and DEM images to extract both types of lineaments, which are compared with real geological structures including faults. The results show the proposed method is superior to the HT method and effective in detection of lineaments reflecting geological structures which are roughly rectilinear and expressed at multiple scales and directions.

Enhancing 2D retention index accuracy: Correcting 2D retention time shifts in GC×GC due to modulation timing deviations via line detection technology.

Abstract Suspended ceiling (SC) systems constitute a critical nonstructural building component. Excessive deformation of the ceiling surface can cause life‐threatening falling debris during earthquakes and create voids that may expose occupants to hazardous materials concealed above the ceiling. To address limitations of the in‐service detection of SC deformation, this paper presents a point cloud–based full‐scene SC detection method, integrating region growing, Hough Transform, a customized Set2Seq network, and robust principal component analysis to achieve a complete workflow from ceiling segmentation, panel extraction to deformation quantification. Point cloud data with color information acquired from two precision‐differentiated devices are used in substage tests and holistic evaluation. The substage tests demonstrate that the local panel deformation quantitative accuracy of the proposed method is generally over 80%, and the holistic experiments show the feasibility of full‐scenario practical application.