Corner Detection Method Research Articles

Smart dimensional quality assessment of embedded steel plates based on images and laser data fusion

Abstract Accurate and efficient positioning is critical to ensuring the dimensional quality assessment of embedded steel plates. However, traditional manual measurement methods struggle to efficiently measure and evaluate these plates. Vision-based measurement methods offer advantages such as high resolution, fast data acquisition, and processing speed, allowing accurate measurement of 2D coordinates. Light Detection and Ranging (LiDAR) can capture highly accurate point clouds, due to the unordered nature of point clouds, processing and analysis require significant computational resources. This paper proposes a method for smart 3D localization of embedded steel plates using image and laser data. (1) We introduce an improved rectangular diagonal constraint Harris corner detection method and achieve subpixel 2D corner detection of embedded plates based on deep learning:(2) given a calibrated camera-LiDAR, we develop a smart detection algorithm guided by 2D image bounding boxes, achieving 3D corner localization. In indoor testing and engineering applications, this method effectively ensures the dimensional quality of embedded steel plates. Compared to traditional manual inspection, the measurement efficiency reaches 10 min per station, with an accuracy of 2.12 mm.

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

يعد تشفير النص في الفيديو أداة حيوية في العديد من الصناعات حيث تكون حماية المعلومات الحساسة هي الأكثر أهمية. عادةً ما تواجه الخوارزميات وطرق الكشف الآلي صعوبة في تحديد موقع النص المخفي في إطارات الفيديو. تزيد هذه المقاومة من أمان المعلومات المخفية لأنه من الصعب على الأطراف غير المدعوة اكتشاف وجود نص مشفر. كما أنه يجمع بين المحتوى المكتوب والمرئي لتقديم كلا النوعين من المعلومات في وقت واحد وبسلاسة. في هذا البحث ، نقدم طريقة جديدة لتشفير النص داخل الفيديو باستخدام خوارزمية التشفير Salsa20 و Harris Corner Detection. الهدف من هذه الدراسة هو زيادة سرية وأمن المعلومات النصية المضمنة في محتوى الفيديو ، وهناك خطوتان أساسيتان في المنهجية المقترحة. لتحديد الزوايا أو النقاط المهمة داخل إطارات الفيديو ، تم استخدام طريقة Harris Corner Detection لأول مرة. تعمل هذه الزوايا كنقاط ميزة قوية يمكن استخدامها لدمج النص. تساعد طريقة Harris Corner Detection في تحديد الأماكن التي قد يتم إخفاء النص بها بدقة ، وذلك دون التقليل من جودة الصورة المرئية الإجمالية للفيديو ، وثانيًا ، يتم إنشاء مفتاح التشفير باستخدام تقنية التشفير Salsa20. Salsa20 هو تشفير دفق شائع يوفر تشفيرًا موثوقًا وتشغيلًا فعالاً. يتم تحويل النص المضمن إلى نص تشفير بواسطة XOR باستخدام مفتاح التشفير قبل تضمينه في الفيديو ، مما يضمن سريته وحمايته من الوصول غير المصرح به. تكشف مقارنة الطريقة المقترحة مع طرق تشفير النص الحالية عن عدد من المزايا. تدمج تقنية Harris Corner Detection النص في إطارات الفيديو بحيث يتم دمجها بشكل أقل وضوحًا ، مما يجعل من الصعب على الخصوم تحديد وجود المعلومات المخفية. يضمن استخدام Salsa20 أيضًا المستوى العالي من الأمان والحماية للنص المشفر ، والدفاع عنه ضد الهجمات المحتملة. يتم تقييم النظام المقترح باستخدام MSE و PSNR والارتباط و NPCR و UACI والإنتروبيا ، وتوفر مقاييس التقييم هذه نتائج ممتازة.

Calibration and pose measurement of a combined vision sensor system for industrial robot grasping of brackets

Recently, visual sensing measurement and its application in industrial robot operations have been widely researched, promoting the development of instrumentation and automation. This study proposes a combined vision sensor system for robot grasping, focusing on combined sensor system calibration and bracket pose measurements. The system configuration and working strategy of the combined vision system are introduced. Thereafter, the calibration of the combined vision coordinate systems is presented, wherein a global vision system acts as the external measuring equipment for accurately calibrating the local vision. Furthermore, a pose estimation method using a local vision system (LVS) is proposed, including morphology-based image enhancement and principal component analysis based corner recognition methods. Verification experiments, including combined calibration and bracket pose measurements, were performed to validate the effectiveness and accuracy of the proposed combined vision measurement strategy. The results demonstrated that the proposed system applies to industrial robot grasping of brackets. In addition, the proposed robot-sensor calibration method improves calibration accuracy. Finally, the proposed corner detection method is effective and accurate for different bracket detection applications. This study provides a system that improves robot grasping results by considering key factors, such as vision measurement accuracy, and calibration methods.

Research on Tire Surface Damage Detection Method Based on Image Processing.

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the "salt and pepper" corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification.

Low-cost camera and 2-D LIDAR fusion for target vehicle corner detection and tracking: Applications to micromobility devices

This paper develops a cost-effective vehicle detection and tracking system based on fusion of a 2-D LIDAR and a monocular camera to protect electric micromobility devices, especially e-scooters, by predicting the real- time danger of a car- scooter collision. The cost and size disadvantages of 3-D LIDAR sensors make them an unsuitable choice for micromobility devices. Therefore, a 2-D RPLIDAR Mapper sensor is used. Although low-cost, this sensor comes with major shortcomings such as the narrow vertical field of view and its low density of data points. Due to these factors, the sensor does not have a robust output in outdoor applications, and the measurements keep jumping and sliding on the vehicle surface. To improve the performance of the LIDAR, a single monocular camera is fused with the LIDAR data not only to detect vehicles, but also to separately detect the front and side of a target vehicle and to find its corner. It is shown that this corner detection method is more accurate than strategies that are only based on the LIDAR data. The corner measurements are used in a high-gain observer to estimate the location, velocity, and orientation of the target vehicle. The developed system is implemented on a Ninebot e-scooter platform, and multiple experiments are performed to evaluate the performance of the algorithm.

Revisiting Mehrotra and Nichani's Corner Detection Method for Improvement with Truncated Anisotropic Gaussian Filtering.

In the early 1990s, Mehrotra and Nichani developed a filtering-based corner detection method, which, though conceptually intriguing, suffered from limited reliability, leading to minimal references in the literature. Despite its underappreciation, the core concept of this method, rooted in the half-edge concept and directional truncated first derivative of Gaussian, holds significant promise. This article presents a comprehensive assessment of the enhanced corner detection algorithm, combining both qualitative and quantitative evaluations. We thoroughly explore the strengths, limitations, and overall effectiveness of our approach by incorporating visual examples and conducting evaluations. Through experiments conducted on both synthetic and real images, we demonstrate the efficiency and reliability of the proposed algorithm. Collectively, our experimental assessments substantiate that our modifications have transformed the method into one that outperforms established benchmark techniques. Due to its ease of implementation, our improved corner detection process has the potential to become a valuable reference for the computer vision community when dealing with corner detection algorithms. This article thus highlights the quantitative achievements of our refined corner detection algorithm, building upon the groundwork laid by Mehrotra and Nichani, and offers valuable insights for the computer vision community seeking robust corner detection solutions.

Checkerboard corner detection method based onneighborhood linear fitting.

To improve the calibration accuracy of a vision measurement system, a checkerboard corner detection method based on linear fitting of the checkerboard local contour is proposed. First, by binarization and morphological dilation of the checkerboard image, the coordinates of two adjacent vertices of adjacent dark squares are obtained; the midpoint of the two vertices is taken as the reference point; the reference dotted array is obtained; and the Zernike moment subpixel method is used to obtain the checkerboard contour data points in the neighborhood of each reference point. Finally, the contour points are classified according to the orientation based on the reference points; two intersecting lines are fitted; and the intersection of the two lines is exactly the corner point that we want to find. A camera calibration experiment was conducted on the same group of checkerboard images. The results show that the calibration results of the corner points obtained based on this method are highly consistent with the OpenCV library function method and the MATLAB Toolbox calibration method, and the reprojection error is smaller. At the same time, it is robust to changes in the light source brightness.

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

AbstractIn the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.

Image Intensity Variation Information for Interest Point Detection.

Interest point detection methods are gaining more attention and are widely applied in computer vision tasks such as image retrieval and 3D reconstruction. However, there still exist two main problems to be solved: (1) from the perspective of mathematical representations, the differences among edges, corners, and blobs have not been convincingly explained and the relationships among the amplitude response, scale factor, and filtering orientation for interest points have not been thoroughly explained; (2) the existing design mechanism for interest point detection does not show how to accurately obtain intensity variation information on corners and blobs. In this paper, the first- and second-order Gaussian directional derivative representations of a step edge, four common genres of corners, an anisotropic-type blob, and an isotropic-type blob are analyzed and derived. Multiple interest point characteristics are discovered. The characteristics for interest points that we obtained help us describe the differences among edges, corners, and blobs, explain why the existing interest point detection methods with multiple scales cannot properly obtain interest points from images, and present novel corner and blob detection methods. Extensive experiments demonstrate the superiority of our proposed methods in terms of detection performance, robustness to affine transformations, noise, image matching, and 3D reconstruction.

Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network

Aiming at the shortcomings of the existing navigation line extraction algorithm for corn spraying robot in complex farmland environment, such as poor extraction effect and poor adaptability, this paper proposed a navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network. This algorithm proposed to use corn plant core as the identification target to locate corn plants. Firstly, a new spatial pyramid structure - atrous spatial pyramid pooling faster (ASPPF) was proposed in this paper, and an improved YOLOv8s model ASPPF-YOLOv8s was proposed for more accurate detection of corn plant cores. Secondly, the center coordinates of the network detection box were used to locate the corn crop row feature points. Finally, the least squares method was used to fit the corn crop row lines. Experimental results showed that ASPPF-YOLOv8s network had a good effect on corn plant cores extraction under different growth periods and environmental pressures. Mean average precision (MAP) and F1 of ASPPF-YOLOv8s network increased from 86.4% and 86% of YOLOv7 network, 88.8% and 87% of YOLOv8s network, 88.6% and 89% of ASPP-YOLOv8s network to 90.2% and 91%. The average fitting time and average angle error of the centerline of crop row were reduced from 82.6 ms and 0.97° for SUSAN corner detection method combined with least square method, 74.8 ms and 0.75° for means method combined with least square method, 67 ms and 2.03° for FAST corner detection method combined with least square method to 45 ms and 0.63° by using the center coordinates of corn plant core detection box combined with least square method. The accuracy rate increased from 91.47% for SUSAN corner detection method combined with least square method, 93.6% for means method combined with least square method and 87.35% for FAST corner detection method combined with least square method to 94.35%. It shows that the navigation line extraction algorithm proposed in this paper can meet the requirements of real-time and accuracy of visual navigation of corn spraying robot, and can be used to extract navigation line of corn spraying robot in complex farmland environment.

Research on the algorithm of image feature detection and matching

In-depth research on feature detection technology affects people's modern life. Modern artificial intelligence can act as the eyes of human beings and efficiently filter out effective information from complex pictures. Corner detection has now evolved into a tool for efficient image scanning. People's increasingly stringent requirements for image processing continue to promote the birth of new technologies. Corner detection methods have been improved and perfected, and have experienced detectors such as Harris, FAST, Scalriant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, AKAZE and Oriented FAST and Rotated BRIEF (ORB). In the face of many mainstream detectors, the main research purpose of this paper is to rely on Python and Computer vision to study the detection efficiency of various detectors in different environments. In this experiment, thirteen groups of pictures were selected, and after being flipped, complicated, and blurred respectively, they were detected by different detectors to obtain the results. Finally, by comparing the feature detection points, detection time and other factors, this study found that the ORB detector can be competent in most situations and is currently the fastest and stable feature point detection and extraction algorithm. On the other hand, the BRISK detector can handle highly blurred images more efficiently.

High-Capacity Spatial Structured Light for Robust and Accurate Reconstruction

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense reconstruction (but less accurate, e.g., speckle-based SL) and accurate reconstruction (but often sparser, e.g., shape-coded SL). The central problem lies in the coding strategy and the designed coding features. This paper aims to improve the density and quantity of reconstructed point clouds by spatial SL whilst also maintaining a high accuracy. Firstly, a new pseudo-2D pattern generation strategy was developed, which can improve the coding capacity of shape-coded SL greatly. Then, to extract the dense feature points robustly and accurately, an end-to-end corner detection method based on deep learning was developed. Finally, the pseudo-2D pattern was decoded with the aid of the epipolar constraint. Experimental results validated the effectiveness of the proposed system.

DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes

The event camera, a new bio-inspired vision sensor with low latency and high temporal resolution, has brought great potential and demonstrated a promising application in machine vision and artificial intelligence. Corner detection is a key step of object motion estimation and tracking. However, most existing event-based corner detectors, such as G-eHarris and Arc*, lead to a huge number of redundant or wrong corners, and cannot strike a balance between the accuracy and real-time performance, especially in complex scenes with high texture that require higher computational costs. To address these issues, we propose an asynchronous corner detection method: a double threshold filter with Sigmoid eHarris (DTFS-eHarris) and an asynchronous corner tracker. The main contributions are that a double threshold filter is designed to reduce the redundant events and the improved Sigmoid function is utilized to represent the Surface of Active Events (Sigmoid*-SAE). We selected four scenes—shapes, dynamic, poster and boxes—from the public event camera dataset DAVIS240C to compare with the existing state-of-the-art hybrid method; our method has shown more than a 10% reduction in false positive rate and a 5% and 20% improvement in accuracy and throughput, respectively. The evaluations indicate that DTFS-eHarris shows a significant improvement, especially in complex scenes. Thus, it is anticipated to enhance the real-time performance and feature detection accuracy for future robotic applications.

A marker-less assembly stage recognition method based on corner feature

Fast and robust product assembly stage recognition is a key step in human–machine cooperative assembly. To solve the recognition problem of similar adjacent assembly stages, a marker-less assembly stage recognition method is proposed based on corner feature between an assembling product and a digital model. Considering the geometric features of mechanical products, a corner identification method is proposed based on the circumferential angle difference (CADF). Then a corner matching method based on distance constraint is studied for ICP registration to realize the point cloud registration between the product and digital model. Based on the registration relationship, a similarity algorithm based on proximity point proportion is used to calculate the similarity between models and the input assembling product. The model with the greatest similarity is taken as the stage recognition result. In experiments on four group assembling products, the average stage recognition accuracy is 96.15%, which indicates that the proposed method can solve the stage recognition problem. The corner identification method based on the CADF outperforms the Harris-3D corner detection method in the efficiency of assembly stage recognition.

LuvHarris: A Practical Corner Detector for Event-Cameras.

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only 'as-fast-as-possible', i.e., only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6× the speed of current state-of-the-art; a necessity when using a high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.

A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network

This study sought an effective detection method not only for flame but also for the smoke generated in the event of a fire. To this end, the flame region was pre-processed using the color conversion and corner detection method, and the smoke region could be detected using the dark channel prior and optical flow. This eliminates unnecessary background regions and allows selection of fire-related regions. Where there was a pre-processed region of interest, inference was conducted using a deep-learning-based convolutional neural network (CNN) to accurately determine whether it was a flame or smoke. Through this approach, the detection accuracy is improved by 5.5% for flame and 6% for smoke compared to when a fire is detected through the object detection model without separate pre-processing.

Development of a Machine Vision System for Image Recognition of Design Estimates

This paper discusses implementation of a machine vision system for pattern recognition of design estimates. The main problem in the development of these systems is the choice of unique features that remain invariant to various kinds of transformations. Angular descriptors were chosen as a dominant feature. The paper presents a comparative analysis of the corner detection methods of the Moravec algorithm, the Harris algorithm, and the Shi-Tomasi algorithm. The authors have developed software in Python language that implements the operation of the Harris detector and the Shih-Tomasi detector. The recognition system is being tested for building 3D models in Blender

Interactive 3D reconstruction method of fuzzy static images in social media

Abstract Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.

An Efficient Corner Detector Using Ratio of Center Distance of Symmetric Contour Technique

Feature detection is one of the basic research topics in the fields of image processing and corner that has been widely applied on vehicle detection, UAV image matching, camera calibration and so on. Particularly, a fast corner detector is of much benefit to many real-time tasks. A novel discrete curvature estimation method for corner detection based on the ratio of center distances of symmetric contour (RCDSC) is proposed in this paper. Benefiting from calculating the Euclidean distance twice only to estimate the discrete curvature at each point on a contour, RCDSC is much fast compared with other corner detectors. In addition, by choosing a relatively large radius of region of support and employing relative distance instead of absolute distance for constructing corner response function, RCDSC is also much robust to noises and local variations of contour. Extensive experiments exhibit the effectiveness and the efficiency of RCDSC in terms of average repeatability (AR), accuracy (ACU) and localization error (LE).

Approach of recognition and precision location for the beacon in satellite optical communications

Satellite optical communications have high communication speed and good confidentiality, which can establish high-speed and large-capacity communication links between satellites and the ground or other satellites. The requirements for the PAT (pointing, acquisition and tracking) system of satellite optical communication terminals are very high. In satellite optical communications, the biggest influence on the beacon’s recognition and tracking is the background light on CCD (Charge Coupled Device). Therefore, denoising work is required during the recognition or tracking process. This paper uses corner detection method to extract the beacon frame by frame. Mark the spot, and use the optical flow method to calculate the optical flow displacement based on the two consecutive frames. In addition, calculation accuracy of the beacon’s centroid is compared with that of the tradition method. The feasibility is verified by simulation experiment, the tracking process is completed, and the noise can be precisely eliminated.