Line Detection Research Articles

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based on YOLOv5x was proposed, which enhances feature information and improves anchor box regression prediction to accurately detect the position of foam lines. Firstly, in the preprocessing stage, employing a rectangular box to simultaneously label the foam line and the edge of the A/O pool within the same region, enhances the feature information of the foam line. Then, the C3NAM module was proposed, which applies weight sparse penalties to attention modules in the feature extraction section, to enhance the capability of extracting foam line features. Subsequently, a B-SPPCSPC module was proposed to enhance the fusion of shallow and deep feature information, addressing the issue of susceptibility to background interference during foam line detection. Next, the Focal_EIOU was introduced to ameliorate the issue of class imbalance in detection, providing more accurate bounding box predictions. Lastly, optimizing the detection layer scale improves the detection performance for smaller targets. The experimental results demonstrate that the accuracy of this algorithm reaches 98.9%, and the recall reaches 88.1%, with a detection frame rate of 26.2 frames per second, which can meet the actual detection requirements of real-world application scenarios.

Hederagenin promotes lung cancer cell death by activating CHAC1-dependent ferroptosis pathway

Lung cancer poses a significant threat globally, especially in China. This puts higher demands on the treatment methods and drugs for lung cancer. Natural plants provide valuable resources for the development of anti-cancer drugs. Hederagenin (Hed) is a triterpenoid compound extracted from ivy leaves and has anti-tumor activity against multifarious cancers, including lung cancer. However, the regulatory mechanism of Hed in lung cancer remains unclear. In this study, we used Hed to treat lung cancer cells, and observed the effect of Hed on cell proliferation (including CCK-8 and colony formation experiments), apoptosis (including flow cytometry and apoptosis gene detection (BAX and Bcl-2)). The results showed that Hed induced lung cancer cell death (inhibiting proliferation and promoting apoptosis). Next, we performed bioinformatics analysis of the expression profile GSE186218 and found that Hed treatment significantly increased the expression of CHAC1 gene. CHAC1 is a ferroptosis-inducing gene. RT-qPCR detection of lung cancer clinical tissues and related cell lines also showed that CHAC1 was lowly expressed in lung cancer. Therefore, we knocked down and overexpressed CHAC1 in lung cancer cells, respectively. Subsequently, cell phenotype experiments showed that down-regulating CHAC1 expression inhibited lung cancer cell death (promoting proliferation and inhibiting apoptosis); on the contrary, up-regulating CHAC1 expression promoted lung cancer cell death. To further verify that Hed exerts anti-tumor effects in lung cancer by promoting CHAC1 expression, we performed functional rescue experiments. The results showed that down-regulating CHAC1 expression reversed the promoting effect of Hed on lung cancer cell death. Mechanistically, in vitro and in vivo experiments jointly demonstrated that Hed exerts anti-cancer effects by promoting CHAC1-induced ferroptosis. In summary, our study further enriches the regulatory mechanism of Hed in lung cancer.

Cascaded-LaneAFA: a single-stage traffic lane line detection network

Cascaded-LaneAFA: a single-stage traffic lane line detection network

TSAR-MVS: Textureless-aware segmentation and correlative refinement guided multi-view stereo

The reconstruction of textureless areas has long been a challenging problem in MVS due to lack of reliable pixel correspondences between images. In this paper, we propose the Textureless-aware Segmentation And Correlative Refinement guided Multi-View Stereo (TSAR-MVS), a novel method that effectively tackles challenges posed by textureless areas in 3D reconstruction through filtering, refinement and segmentation. First, we implement the joint hypothesis filtering, a technique that merges a confidence estimator with a disparity discontinuity detector to eliminate incorrect depth estimations. Second, to spread the pixels with confident depth, we introduce an iterative correlation refinement strategy that leverages RANSAC to generate 3D planes based on superpixels, succeeded by a weighted median filter for broadening the influence of accurately determined pixels. Finally, we present a textureless-aware segmentation method that leverages edge detection and line detection for accurately identify large textureless regions for further depth completion. Experiments on ETH3D, Tanks & Temples and Strecha datasets demonstrate the superior performance and strong generalization capability of our proposed method.

Enhancing Power Lines Detection Using Deep Learning and Feature-Level Fusion of Infrared and Visible Light Images

AbstractThe detection of power lines is critical for flight safety, especially for drones and low-flying aircraft. Power line detection models help prevent collisions, reducing potential damage and preserving lives, while also safeguarding critical infrastructure. This has led to significant research efforts to develop accurate detection models. In this study, we employ paired infrared–visible power line datasets to train three distinct deep learning models. The first two models are sequential deep learning models based on VGG16 and AlexNet networks. They are tailored for detection in visible images, while they were optimized again for infrared images. For the third model, we introduce an innovative deep learning architecture utilizing Functional Application Programming Interface, affording us the flexibility to construct a multi-input model with shared layers. Our proposed model accepts paired images (visible and infrared) as inputs. Then, a feature-level fusion process is applied to merge the extracted features from both inputs and generate an enriched feature map. This approach amalgamates the advantages of visible images, which boast high resolution and rich texture features, with infrared images, which excel in high contrast and clear vision under adverse environmental conditions. Comparing the outcomes of the three models, our proposed model emerges as the front runner, boasting an impressive accuracy rate of 99.37%. Moreover, real-time processing was adopted by conducting ablation experiments to optimize the model and reduce the number of trainable parameters, resulting in an inference speed of 2.7 milliseconds per frame.

Robust Lane Detection Method Based on Dynamic Region of Interest

Lane detection is an important component of vehicle assisted driving systems. Many lane line algorithms have been proposed, but lane line detection is still a challenging task in environments with road text or vehicle interference. To address these issues, this paper proposes a robust lane line detection method under structured roads. First, we propose a method based on the slope and length of a straight line and the distance of the line from the centre line to extract regions of interest reducing interference from other invalid regions. Afterwards, we use a new fusion method to fuse the extracted colour features with the gradient features, enhancing the lane features while greatly reducing the interference in the pixels. Next, we cut the extracted pixel feature maps in equal parts into several sub-maps and perform pixel statistics for each sub-map, replacing sub-maps with abnormal pixel values with adjacent sub-maps, which can effectively reduce noise. Finally, we use DBSCAN to cluster the pixels and fit the lane lines with least squares. The experimental results show that the method can effectively detect lane lines and has good robustness.

Power Line Detection Based on Subpixel-Neighborhood Attention in Complex Terrain Backgrounds

Power Line Detection Based on Subpixel-Neighborhood Attention in Complex Terrain Backgrounds

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

Multi-Purpose Tracker Robot

Abstract: This paper introduces a multi-purpose tracking robot designed with Arduino, ultrasonic, infrared sensors, and motor drivers. The robot is capable of performing diverse tasks such as obstacle avoidance, line following, and object tracking. Its hardware comprises an Arduino Uno microcontroller, ultrasonic sensors for distance measurement, infrared sensors for line detection and obstacle avoidance, and motor drivers for controlling DC motors. The software, developed using Arduino IDE, includes algorithms for sensor data processing, decision-making logic, and motor control. By implementing PID control algorithms, the robot achieves enhanced stability and accuracy in navigation tasks like motor speed control and line following. Experimental results validate its effectiveness in various scenarios, showcasing its potential for advanced robotics and automation applications through intelligent sensor integration and control strategies.

Lane Line Detection

Abstract: Lane line detection is a crucial component in the development of advanced driver assistance systems (ADAS) and autonomous vehicles. This research proposes a comprehensive approach to enhance the accuracy and robustness of lane line detection algorithms, addressing challenges such as varying road conditions, lighting conditions, and diverse road markings.The proposed methodology leverages computer vision techniques, including edge detection, color space transformations, and image filtering, to preprocess input images from onboard cameras. A novel algorithm for lane line extraction is introduced, combining feature extraction and geometric analysis to accurately identify lane markings. The algorithm adapts to different road scenarios by dynamically adjusting parameters based on environmental conditions. The proposed lane line detection system demonstrates promising results in terms of accuracy, adaptability, and real-time performance, showcasing its potential for practical implementation in autonomous vehicles and ADAS. The research contributes to the ongoing efforts in creating robust and reliable perception systems for safe and efficient autonomous navigation on roads.

YOLO‐AFPN: Marrying YOLO and AFPN for external damage detection of transmission lines

AbstractTo better detect targets that may cause external damage to transmission lines, the authors present You Only Look Once‐Asymptotic Feature Pyramid Network (YOLO‐AFPN), a lightweight but efficient model. Firstly, the authors adopt a feature comparison strategy based on the knowledge of transmission line scenes, which facilitates increased attention to target features during the training. Secondly, the YOLOv8 detection network is built, and the backbone adds three layers of simple parameter‐free attention module, which extracts features while maintaining lightness, and improves the detection capability in complex scenarios. Then, in the feature fusion stage, an AFPN is constructed, which improves the multi‐scale target detection capability while reducing the number of model parameters by asymptotically fusing features that have small semantic gaps between neighbouring layers. When during the training process, the improved Mosaic data augmentation method is used to enhance the number of distributions of small targets, improve the robustness of the model. Finally, the improved model is validated, and the experimental results show that the improved model can achieve mean average precision of 86.1% at 6.6 MB, which is better than the original network for detection and meets the requirements for deployment on edge devices.

OpenCV-based Lane Line Detection Method for Mountain Curves

In this paper, a new method based on the OpenCV computer vision library is proposed and used for the detection of mountain bike lane lines. Firstly, a series of pre-processing, HLS color filtering, grayscale map conversion, etc. are performed on the images captured by the on-board camera. Next, the lane line image is processed by Gaussian blur noise reduction, Canney edge detection and other techniques. Then, after masking for the region of interest, the lane lines are fitted after applying the Hough transform to obtain a lane line image that can be used for detection. Finally, to enhance the visual effect, the fitted lane line image is filled and displayed superimposed with the original image. The experimental results show that the proposed method has high detection accuracy and efficiency in general mountain road scenarios, and it plays a certain role in assisting the driver to drive.

New Insights on the Accretion Properties of Class 0 Protostars from 2 μm Spectroscopy

Sun-like stars are thought to accrete most of their final mass during the protostellar phase, during which the stellar embryo is surrounded by an infalling dense envelope. We present an analysis of 26 K-band spectra of Class 0 protostars, which are the youngest protostars. Of these, 18 are new observations made with the Keck MOSFIRE instrument. H i Brγ, several H2, and CO Δv = 2 features are detected and analyzed. We detect Brγ emission in 62%, CO overtone emission in 50%, and H2 emission in 90% of sources. The H i and CO emission is associated with accretion, while the H2 lines are consistent with shock excitation indicating jets/outflows. Six objects exhibit photospheric absorption features, with almost no outflow activity and no detection of the accretion-related Brγ emission line. Comparing these results with an archival sample of Class I K-band spectra, we find that the CO and Brγ emission lines are systematically more luminous in Class 0s, suggesting that the accretion is on average more vigorous in the Class 0 phase. Typically associated with the heated inner accretion disk, the much higher detection rate of CO overtone emission in Class 0s indicates also that episodes of high accretion activity are more frequent in Class 0 systems. The kinematics of the Class 0 CO overtone emission suggest either an accretion-heated inner disk or material directly infalling onto the central region. This could point toward an accretion mechanism of different nature in Class 0 systems than the typical picture of magnetospheric accretion.

Detection of Rydberg Lines from the Atmosphere of Betelgeuse

Emission lines from Rydberg transitions are detected for the first time from a region close to the surface of Betelgeuse. The H30α line is observed at 231.905 GHz, with an FWHM ∼42 km s−1 and extended wings. A second line at 232.025 GHz (FWHM ∼21 km s−1 ), is modeled as a combination of Rydberg transitions of abundant low first ionization potential metals. Both H30α and the Rydberg combined line X30α are fitted by Voigt profiles, and collisional broadening with electrons may be partly responsible for the Lorentzian contribution, indicating electron densities of a few 108 cm−3. X30α is located in a relatively smooth ring at a projected radius of 0.9× the optical photospheric radius R ⋆, whereas H30α is more clumpy, reaching a peak at ∼1.4 R ⋆. We use a semiempirical thermodynamic atmospheric model of Betelgeuse to compute the 232 GHz (1.29 mm) continuum and line profiles making simple assumptions. Photoionized abundant metals dominate the electron density, and the predicted surface of continuum optical depth unity at 232 GHz occurs at ∼1.3 R ⋆, in good agreement with observations. Assuming a Saha–Boltzmann distribution for the level populations of Mg, Si, and Fe, the model predicts that the X30α emission arises in a region of radially increasing temperature and turbulence. Inclusion of ionized C and non-LTE effects could modify the integrated fluxes and location of emission. These simulations confirm the identity of the Rydberg transition lines observed toward Betelgeuse and reveal that such diagnostics can improve future atmospheric models.

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

NuSTAR detection of a broad absorption line in IGR J06074+2205

BeXRB pulsar IGR J06074+2205 using NuSTAR observations. The temporal and spectral characteristics of the source are investigated. We detect coherent X-ray pulsations of the source and determine the spin period evolution. Using the current spin period data of the source, we show that the source is spinning down at 0.0202(2) sday−1. The pulse profiles are found to evolve with energy and luminosity. Another interesting feature of the source is that the pulse profiles are dual peaked. The dual-peaked pulse profile is characterized by a decreasing secondary peak amplitude with increasing energy. We also observed a proportionate increase in the primary peak amplitude as the energy increases. The pulse fraction exhibits an overall increasing trend with the energy. The X-ray continuum of the source indicates the existence of a characteristic absorption feature with a centroid energy ∼55 keV, which may be interpreted as a cyclotron line. We estimate the corresponding magnetic field strength to be ∼4.74×1012 G. A peculiar ‘10 keV’ absorption feature is observed in the X-ray spectra of the second observation. The luminosity measurements predict that the source may be accreting in the sub-critical regime.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system.

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

The Extraction of Roof Feature Lines of Traditional Chinese Village Buildings Based on UAV Dense Matching Point Clouds

Traditional Chinese buildings serve as a carrier for the inheritance of traditional culture and national characteristics. In the context of rural revitalization, achieving the 3D reconstruction of traditional village buildings is a crucial technical approach to promoting rural planning, improving living environments, and establishing digital villages. However, traditional algorithms primarily target urban buildings, exhibiting limited adaptability and less ideal feature extraction performance for traditional residential buildings. As a result, guaranteeing the accuracy and reliability of 3D models for different types of traditional buildings remains challenging. In this paper, taking Jingping Village in Western Hunan as an example, we propose a method that combines multiple algorithms based on the slope segmentation of the roof to extract feature lines. Firstly, the VDVI and CSF algorithms are used to extract the building and roof point clouds based on the MVS point cloud. Secondly, according to roof features, village buildings are classified, and a 3D roof point cloud is projected into 2D regular grid data. Finally, the roof slope is segmented via slope direction, and internal and external feature lines are obtained after refinement through Canny edge detection and Hough straight line detection. The results indicate that the CSF algorithm can effectively extract the roofs of I-shaped, L-shaped, and U-shaped traditional buildings. The accuracy of roof surface segmentation based on slope exceeds 99.6%, which is significantly better than the RANSAC algorithm and the region segmentation algorithm. This method is capable of efficiently extracting the characteristic lines of roofs in low-rise buildings within traditional villages. It provides a reference method for achieving the high-precision modeling of traditional village architecture at a low cost and with high efficiency.

Design and Evaluation of a Double-Gate Tunnel Field Effect Transistor for the Detection of Breast Cancer Cells

This research paper investigates the application of a Double Gate (DG) Tunnel Field-Effect Transistor (DG-TFET) for the detection of cell lines derived from breast cancer tissue, namely Hs578T, MDA-MB-231, MCF-7, and T47D. The device incorporates two nanocavities positioned beneath the two gate electrodes, significantly enhancing detection capabilities. The study emphasizes the differentiation between healthy non-tumorigenic cells (MCF-10A) and breast cancer-derived cell lines by incorporating gate engineering into the TFET. Furthermore, the research explores the impact of changes in dielectric values specific to different breast malignant cell types on the biosensor's detection capabilities. Additionally, the investigation delves into the influence of variations in device geometry, including cavity dimensions and dielectric layer thickness, on critical parameters such as drain current sensitivity, transconductance sensitivity, and ION/IOFF sensitivity. Sensitivity analysis concerns drive current, ION/IOFF ratio, threshold voltage (Vth), and transconductance. The structural design of the device is tailored to facilitate array-based diagnosis and screening of cell lines derived from breast cancer tissue. This design offers several advantages, including a simplified transduction process, compatibility with CMOS processes, cost-effectiveness, reproducibility, and adjustable electrical responses. The researchers employed ATLAS, a two-dimensional (2D) device simulator from Silvaco, to model and define the device structure. The numerical simulations validate the device's performance, demonstrating favorable ON-OFF transition profiles.

Otsu-BRSG: An Effective Algorithm for River Bank Line Detection and Monitoring in the Challenging Terrains of Kaziranga National Park

Otsu-BRSG: An Effective Algorithm for River Bank Line Detection and Monitoring in the Challenging Terrains of Kaziranga National Park