How to detect curved lines and straight lines using Hough Transform?
Best insight from top research papers
To detect curved lines and straight lines using the Hough Transform, different approaches can be employed. For straight-line detection, methods like dividing the image into boxes with high-intensity pixels for efficient processing , and incorporating trainable Hough transform blocks into deep networks to learn global line parameterizations and local features are effective. On the other hand, for curved line detection, adjusting thresholds to distinguish between straight and catenary lines in transmission line monitoring images using the Hough Transform is crucial . Additionally, for circle detection, implementing a Hough transform on threshold edge maps after edge recognition can be beneficial . These diverse strategies showcase the versatility of the Hough Transform in detecting various types of lines in images.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| The paper introduces Deep Hough-Transform Line Priors, combining trainable Hough transform for global line priors and deep networks for local features, enhancing line detection efficiency with prior knowledge. | |
Open access•Posted Content | The paper introduces Deep Hough-Transform Line Priors, combining trainable Hough transform for global line priors and deep networks for local features, enhancing line segment detection efficiency with prior knowledge. |
| Hough Transform detects straight lines using angle-radius parameters and curved lines using a function like "houghcircle" on threshold edge maps, aiding in shape recognition in images. | |
17 Oct 2020 1 Citations | The Hough Transform can detect curved and straight lines by adjusting thresholds to remove interference, as demonstrated in monitoring transmission line images in the paper. |
01 Jan 2022 3 Citations | Not addressed in the paper. |
Related Questions
What is the best way to detect leaks in underground water pipes?5 answersThe best way to detect leaks in underground water pipes is by using various technologies such as infrared temperature sensors and ground acoustic signals. Ameen Awwad et al. proposed the use of an unmanned ground vehicle (UGV) equipped with an infrared temperature sensor to remotely detect thermal anomalies on the surface caused by underground leaking pipelines. Peng Zhang et al. introduced a novel approach to leak detection using ground acoustic signals and a pseudo-siamese convolutional neural network (PCNN) model that combines handcrafted features and deep representations. Fahed Ebisi et al. explored the effectiveness of three different anomaly detection schemes, including the unsupervised multi-variate classification known as Isolation Forest (iForest), the supervised classification approach using Support Vector Classification (SVC), and a deep learning RNN-LSTM model. These methods provide insights into the effective applicability of sensors and machine learning algorithms for detecting water leaks in underground pipes.
How to use hough transform and cnn for crop row detection?5 answersThe Hough transform (HT) and CNN are used for crop row detection in precision agriculture. One approach combines image evidence and prior knowledge about the geometric structure to accurately detect both straight and curved crop rows. Another approach uses the HT with a modified segmentation technique, such as the Robust Fuzzy Possibilistic C-Means (RFPCM), to detect lines in row crops for navigation purposes. Additionally, a CNN-transformer approach is proposed for crop classification using multitemporal Earth observation data. This approach utilizes the transformer architecture to extract deep correlation patterns from multitemporal sequences and achieve significant performance improvement compared to traditional methods. These methods provide reliable and accurate results for crop row detection in various crop growth stages and different types of crops.
How do fingerprint detectors work?5 answersFingerprint detectors work by using under-screen optical fingerprint detection technology. The detectors are placed under the display screen of an electronic device and include an optical component and an optical sensor. The optical component directs or converges a fingerprint detection signal to the optical sensor, which detects corresponding fingerprint information based on the signal. The fingerprint detection signal is a reflected light signal formed by a light signal corresponding to a predetermined pattern on the display screen, which is reflected by the user's finger. The predetermined pattern consists of a first pattern and a second pattern, with the first pattern being closer to the center of the fingerprint detection area than the second pattern. The signal intensity of the first optical signal corresponding to the first pattern is smaller than the signal intensity of the second optical signal corresponding to the second pattern.
How can the straight line method be used to predict future values?5 answersThe straight line method can be used to predict future values by fitting a straight line to the existing data points and using the equation of the line to estimate the values for new data points. This method is commonly used in various fields such as hydrogeology, image processing, and data analysis. In hydrogeology, the Cooper-Jacob straight line is used to estimate aquifer parameters from late time-drawdown data. In image processing, a straight line detection method is used to find the most suitable splicing lines and obtain a straight line detection result. In data analysis, the maximum likelihood method is applied to straight line regression to calculate the line parameters and their uncertainties. By using the straight line method, future values can be predicted based on the trend observed in the existing data.
What are the latest methods for fault detection in transmission lines?5 answersRecent methods for fault detection in transmission lines include the following:
1. A method proposed by Sudhakar et al. uses MATLAB software to simulate various fault scenarios on transmission lines and analyze the type of fault. They also design a hardware model using actuating relays and a microcontroller to detect and display fault parameters.
2. Wang et al. present a fault detection method based on a multi-source data fusion algorithm. They use the component symmetry between forward and inverse traveling waves to discriminate abnormal regions and construct a tripping warning model. Experimental results show improved detection accuracy when combined with the multi-source data fusion algorithm.
3. Jena and Pradhan propose two protection techniques for transmission lines using information from the decaying DC component of fault current and the pre-fault voltage signal. These techniques accurately detect internal faults within a quarter cycle and have been tested on a simulated transmission system.
4. Another approach suggested by an anonymous author involves using machine learning techniques to detect and classify faults in transmission lines. Multiple classifiers are trained and tested to provide accurate fault predictions, enhancing safety and reliability.
What are the different methods for detecting fault lines?5 answersDifferent methods for detecting fault lines include a hybrid method based on Machine Learning (ML) techniques, a fault detecting method based on route trajectory and concentration analysis, a line fault detection method using redundant channels, a method based on continuously monitoring voltage characteristics and generating crowbar trigger activation signals, and a distribution lines fault detection device. The hybrid method uses Wavelet Transform (WT) and GoogLeNet model for fault identification and classification, and Convolutional Neural Network (CNN) for fault location. The fault detecting method determines fault nodes based on trajectory concentration exceeding a threshold. The line fault detection method uses redundant channels to transmit fault information and determines faults based on valid fault conditions. The method based on monitoring voltage characteristics generates crowbar trigger activation signals to disconnect circuitry based on fault conditions. The distribution lines fault detection device includes a current transformer and sealed cowling for accurate and stable fault detection.