Corner Point Detection Research Articles

Design and Implementation of Fabric Wrinkle Detection System Based on YOLOv5 Algorithm

Background Nowadays, robots have been widely used in handling rigid objects, but research on deformable objects like fabrics is still in its early stages. This is because fabrics possess infinite degrees of freedom and their state modeling is highly complex, making robot manipulation of fabrics challenging due to the occurrence of wrinkles and deformations during the operation. The detection and recognition of fabric deformations such as wrinkles and fabric manipulation features like corners are of great significance in enhancing a robot's capability to handle deformable objects. Methods In response to the issue of fabric wrinkles in various scenarios, we propose a real-time fabric wrinkle and corner detection system based on the YOLOv5 detection algorithm. Additionally, we implement a fabric flattening operation on a hardware platform using the detected wrinkle and corner information. Results We collected and created a dataset of fabric deformation features and trained a detection model, achieving a detection accuracy of over 90%. The model was deployed in the fabric wrinkle detection system, using a heuristic operation strategy of flattening the fabric from the four corners. As a result, the robot successfully performed the flattening operation on wrinkled fabric. Conclusions The application of the YOLOv5 algorithm enables effective detection of fabric wrinkles and corner points. Based on the detection information and using the quadrilateral flattening operation method, the robotic system achieves fabric flattening operations.

Gradient-Based Optimization Method for Experimental Modal Parameter Estimation with Finite Element Model

This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete vibration response equation into a matrix form and formulate the parameter estimation problem in modal analysis as an optimization problem. Then the problem is solved with a gradient-based iterative algorithm, which explicitly exhibits the closed form of gradients used in optimization. Initial values for this iteration are parameters derived from finite element models, since every important engineering structure should be analyzed with a finite element model before it is constructed. Subsequently, the performance of this algorithm is validated by both pure numerical experiments, which simulate the physical world, and experiments using real measurement data gathered by sensors in the real physical world. The algorithm’s performance is further enhanced by incorporating gradient clipping and an adaptive iteration threshold. As a comparison, a discussion on classical least-squares time-domain method for the problem is provided. For practical applications, the Shi–Tomasi corner detection and Lucas–Kanade optical flow methods are deployed to detect corner points from videos taken during the vibration of a structure and track the motion of these points in the videos.

Improved fiber orientation measurement in nonwovens with corner removal

The orientation of fibers or filaments in nonwovens is critical in determining their mechanical characteristics. Image processing techniques, prized for their minimal human intervention and rapid processing speed, are widely utilized in nonwoven fiber orientation measurement. However, these techniques often face substantial challenges, such as low accuracy in corner detection, errors in fiber segmentation, and inefficiencies in fiber orientation calculation. Addressing these concerns, this study introduces a novel, enhanced method accompanied by two innovative optimization algorithms to enhance accuracy. The first innovation involves the development of a newly developed fiber corner detection algorithm, dubbed the T-detector, specifically tailored for the unique characteristics of fiber images, enabling efficient corner point detection and removal. Subsequently, we introduce and employ a fiber length restriction algorithm to further segment the processed longer fibers into the remaining fiber fragments and utilize a skeleton projection algorithm to calculate the fiber orientation. These algorithms overcome the existing technology’s inherent shortcomings, thereby heightening measurement accuracy. The results illustrate an improvement in measurement precision over other orientation distribution measurement algorithms, with the fiber information retention (covering ratio) reaching an impressive 95%. Our proposed method not only calculates fiber orientation distribution in nonwovens with remarkable accuracy and efficiency, but its innovative approach also stands to provide a theoretical foundation for the design of three-dimensional filtering models with specific fiber orientation.

Shape Recognition and Corner Points Detection in 2D Drawings Using a Machine Learning Long Short-Term Memory (LSTM) Approach

Creating a 2D geometry model from an image poses challenges for CAD users due to factors such as noise, segmentation difficulties, complex geometric structures, scale and perspective variations, and the need for CAD system compatibility. In this paper, we propose a novel deep learning approach utilizing Long-Short Term Memory (LSTM) to address these challenges. Our approach decomposes the shapes in the images into line and curve segments and accurately locates their intersection points. To enhance the model’s performance, we introduce two distinct types of features (angle and curvature features) and optimize the model through hyperparameter tuning. The resulting model exhibits robustness against noise, varying image sizes, and can effectively locate different types of intersection points. To evaluate the proposed model, we have developed a Python-based software and conducted experiments on a dataset comprising of 200 shapes with seven different resolutions. Comparative analysis against a state-of-the- art method (TCVD) from the literature demonstrates that our approach achieves higher accuracy in terms of line, curve, and intersection point detection.

Improved Shi-Tomasi sub-pixel corner detection based on super-wide field of view infrared images.

In this work, we propose a new, to the best of our knowledge, corner point detection method for a super-wide field of view infrared imaging system. First, the edge of the checkerboard calibration board is detected at the pixel level by morphological operation. Second, the interpolation technique is used to refine the edge so that the edge has sub-pixel accuracy. We obtain the four checkerboard unit corners near the real corner point and average the coordinates of the four corners to indirectly obtain the coordinates of the real corner point. Meanwhile, we take pictures of the same calibration board at different angles for repeatability verification. It is proved that the improvement of our algorithm for the detection of corners of super-wide field of view infrared images is more feasible compared to the traditional algorithms.

Research on the application of machine vision in the design of machine automation

Abstract The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

A preliminary investigation of niche clothing brand design optimization based on ADDIE model

Abstract This paper utilizes the ADDIE model and feature extraction method to analyze and study the design of niche clothing brands. Through the study of the ADDIE model, the model’s components are applied to the design of niche clothing brands. The three elements affecting the design of clothing brands are studied, and the main reasons affecting niche clothing brands are explored through the analysis of the integrity, normality and planning of clothing design. Combining color feature extraction methods to calculate moments is used to describe the color distribution of the images. The results show that the niche clothing brand design has great uniqueness compared to the mass clothing brand design, with an embodiment rate of 75%. Compared to other feature extraction methods, the detection rate for color feature extraction is 65%, corner point detection is 70%, and speckle detection is 59%.

Application of Automatic Recognition and Positioning Technology of Industrial Robot based on Monocular Vision Technology in Vehicle Manufacturing

Combining machine vision technology with intelligent algorithms to improve the automatic identification and positioning technology of industrial robots to meet the production needs in different industrial environments is the current research direction of intelligent robots. In this study, an automatic identification and positioning technology for industrial robots based on monocular vision technology is proposed. First, the template matching algorithm and the Scale-invariant feature transform (SIFT) algorithm are introduced. Aiming at the shortcomings of the Hessian matrix in the SIFT algorithm in the process of eliminating boundary effects, an improved SIFT algorithm using Harris corner point detection is further proposed, and the improved SIFT algorithm is used in the automatic recognition and target positioning operations of industrial robots. In order to verify the performance of the proposed improved SIFT algorithm, the recognition accuracy and positioning angle deflection of the algorithm in different plastic sheets were detected. The experimental results show that under the improved SIFT algorithm, the recognition accuracy of five different styles of plastic sheets is above 98%. The improved SIFT algorithm also has less error between the predicted value and the actual value of the positioning angle deflection on the four plastic sheets. The robot under the improved algorithm is applied to the vehicle manufacturing industry and the production efficiency of the vehicle is improved through the automatic recognition and positioning technology of intelligent robot.

Deep learning-based autonomous real-time digital meter reading recognition method for natural scenes

Natural scenes with variable illumination, variable target scale and angular tilt pose significant challenges to the autonomous recognition of digital meter readings. Based on this, this paper proposes a deep learning-based autonomous real-time digital meter reading recognition method for natural scenes. First, the YOLO-style corner point detection method (YOLO-CPDM) for the reading area is proposed by reconstructing the detection heads and incorporating the corner detection loss function. Its localization accuracy is further refined by embedding attention mechanism module, implementing dynamic loss function and enhancing training data diversity through offline augmentation techniques like image rotation and flipping. Then, the detected corner points are used to geometrically correct the distorted reading area by perspective transformation to mitigate the interference caused by the shooting angle. Next, the YOLO-style end-to-end reading recognition method (YOLO-EERRM) is proposed to accurately extract the characters in the reading area. Finally, the validity of the YOLO-CPDM and YOLO-EERRM was verified on a produced dataset named SYSU-DM and 2 public datasets. Compared with the State of the arts (SOTA) keypoint detection model, the mean Average Precision @ 50:95 scores of the YOLO-CPDM improved by 2.8, 4.1, and 1.1 points, respectively, while the inference latency was only 5.3 ms, and YOLO-EERRM achieved 100 % accuracy and 3.1 ms inference latency on the SYSU-DM dataset. Statistically, the complete digital meter reading recognition method has 99.6 % accuracy and 8.6 ms inference latency, indicating that the system has high recognition accuracy and practicality.

Adaptive dewarping of severely warped camera-captured document images based on document map generation.

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system's reliability by demonstrating improved readability even for severely distorted image samples.

High-Precision Binocular Camera Calibration Based on Super-Resolution Corner Point Detection Algorithm

High-Precision Binocular Camera Calibration Based on Super-Resolution Corner Point Detection Algorithm

Real-time detection of crop rows in maize fields based on autonomous extraction of ROI

The current crop rows detection based on machine vision generally has the problems of low detection accuracy and poor real-time performance. Moreover, crop rows detection remains a challenging problem in complex field conditions, such as high weeds pressure, poor illumination conditions, and vegetation foliage shading. We propose a crop rows detection algorithm based on autonomous extraction of ROI (Region of interest). The prior method computes the feature points of the entire image and groups them into the crop rows which they belong to. Instead, we consider the core of crop rows detection problem to be the extraction of the travelling area of agricultural machinery in maize fields. A YOLO (You Only Look Once) neural network is employed to predict the travelling area of the agricultural machinery end-to-end. The prediction boxes are unified into ROI and the crop and soil background are segmented in the ROI by Excess Green operator and Otsu’s method. Then, the feature points of crops are extracted using FAST (Features from Accelerated Segment Test) corner point detection, and finally the detection lines of crop rows are fitted with least squares method. Because image recognition is limited to a valid region after the ROI is extracted, the processing speed of our algorithm is remarkably fast. It takes only about 25 ms to process a single image (640*360 pixels) and the frame rate of video stream exceeds 40FPS. Meanwhile, it can achieve high accuracy and robust extraction of ROI in various maize fields. The average error angle of the detection lines is 1.88°, which can meet the real-time and accuracy requirements of field navigation. The proposed algorithm can provide a new solution to the current machine vision-based navigation technology for agricultural machinery. Code is available at: https://github.com/WoodratTradeCo/crop-rows-detection.

Distributed Small-Step Path Planning and Detection Method for Post-earthquake Robot to Inspect and Evaluate Building Damage.

Post-earthquake robots can be used extensively to inspect and evaluate building damage for safety assessment. However, the surrounding environment and path for such robots are complex and unstable with unexpected obstacles. Thus, path planning for such robot is crucial to guarantee satisfactory inspection and evaluation while approaching the ideal position. To achieve this goal, we proposed a distributed small-step path planning method using modified reinforcement learning (MRL). Limited distance and 12 directions were gridly refined for the robot to move around. The small moving step ensures the path planning to be optimal in a neighboring safe region. The MRL updates the direction and adjusts the path to avoid unknown disturbances. After finding the best inspection angle, the camera on the robot can capture the picture clearly, thereby improving the detection capability. Furthermore, the corner point detection method of buildings was improved using the Harris algorithm to enhance the detection accuracy. An experimental simulation platform was established to verify the designed robot, path planning method, and overall detection performance. Based on the proposed evaluation index, the post-earthquake building damage was inspected with high accuracy of up to 98%, i.e., 20% higher than traditional unplanned detection. The proposed robot can be used to explore unknown environments, especially in hazardous conditions unsuitable for humans.

EXTRACTION OF ORTHOGONAL BUILDING BOUNDARY FROM AIRBORNE LIDAR DATA BASED ON FEATURE DIMENSION REDUCTION

Abstract. Building boundary extraction is an active research topic in the field of feature extraction from airborne LiDAR point cloud data. Owing to the high complexity of most building extraction algorithms based on point clouds, multiple feature parameters must often be combined with iterative operations, particularly in the process of mitigating the sawtooth phenomenon using the sleeve algorithm and its improved versions. To improve the degree of automation and ensure accuracy, this study proposes a fast corner point detection method based on a dimensionality reduction technique, which utilizes reduced data mapping from 3D to 2D. We converted the boundaries extracted by the alpha shape algorithm to a 2D image and applied recursive Gaussian filtering with a relatively high level of automation to smoothen the image edges and mitigate the sawtooth phenomenon, thereby improving upon the sleeve algorithm, which requires a large number of iterations. Subsequently, the Douglas Peucker algorithm is used to retrieve the contour key points after extracting the contour lines and obtaining the regularized building contours using the grouped orthogonal regularization method. To verify the accuracy of the algorithm, it was compared with a cluster and adjustment (CAA)method based on the sleeve algorithm using three major evaluation metrics with respect to four representative building instances in two experimental datasets of urban areas. The value of the RMSE was reduced by an average of 43.79%. In addition, the time complexity decreased from O(n2) to O(n). These results demonstrate that the proposed method improves not only the accuracy of boundary extraction, but also the efficiency of data processing.

A Fast Circle Detector with Efficient Arc Extraction

Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods.

Angle detection system for crossing compression of retinal artery and vein

The crossing compression of retinal artery and vein is closely related to retinal vein occlusion, so detecting the contraction angle of the crossed vein blood vessel can assist to diagnose the retinal vein occlusion diseases. Through pretreating methods such as filtering, enhancement and edge extraction, the binary edge images can be extracted. The candidate feature points can be obtained by the corner point detection method based on chord-to-point distance accumulation (CPDA). The self-adaptive rectangular filter is used to screen out the crossing point of candidate angle, so as to fit the edge curves and figure out the contraction degree of vein. The experimental results show that this algorithm can better detect the contraction degree of crossed vein blood vessel, with an average error remaining at ± 1∘ under different resolutions.

Methods on Visual Positioning Based on Basketball Shooting Direction Standardisation

The existing basketball training shooting direction correction method has the problems of low correction accuracy and poor self-adaptability, and proposes a basketball training shooting direction correction method based on visual perception. A visual localisation algorithm for tracking feature points of object targets is used as the basis for the process of visual robot localisation and its effects, from camera calibration, template matching, background modelling and foreground target separation to feature point extraction, motion estimation, and Kalman filtering. An in-depth understanding and analysis of the traditional corner point detection algorithm is presented, on the basis of which improvements are proposed. An accurate tracking method based on improved Harris corner point extraction is introduced, which builds on the traditional Harris feature point detection by using the changing relationship between the gradient of the grey value of the pixels near the corner point, using simple operations and analysis to exclude some pseudocorner points and noncorner points, and further processing the retained points to derive the correct feature points. The code of this algorithm is written to finally achieve its detection effect, and compared with the traditional algorithm, it is concluded that this algorithm can then extract more accurate corner points in a shorter time, which lays the foundation for the next step of accurate basketball tracking, reflecting the practicality of this algorithm.

A Poisson Equation-Based Method for 3D Reconstruction of Animated Images

3D reconstruction techniques for animated images and animation techniques for faces are important research in computer graphics-related fields. Traditional 3D reconstruction techniques for animated images mainly rely on expensive 3D scanning equipment and a lot of time-consuming postprocessing manually and require the scanned animated subject to remain in a fixed pose for a considerable period. In recent years, the development of large-scale computing power of computer-related hardware, especially distributed computing, has made it possible to come up with a real-time and efficient solution. In this paper, we propose a 3D reconstruction method for multivisual animated images based on Poisson’s equation theory. The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. The experimental results show that the proposed method can obtain the 3D reconstruction results of multivisual animation images quickly and accurately and has certain real-time and reliability.

Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system

This paper proposes a novel method for six degrees of freedom pose estimation of objects for the application of robot arm pick and place. It is based on the use of a stereo vision system, which does not require calibration. Using both cameras, four corner points of the object are detected. A deep-neural-network (DNN) is trained for the prediction of the 6 DOF pose of the object from the four detected corner points’ coordinates in each image of both cameras. The stereo vision used is a low-end vision system placed in a custom-made setup. Before the training phase of the DNN, the robot is set to auto collect data in a predefined workspace. This workspace is defined dependently on the spatial feasibility of the robot arm and the shared field of view of the stereo vision system. The collected data represent images of a 2D marker attached to the robot arm gripper. The 2D marker is used for data collection to ease the detection of the four corner points. The proposed method succeeds in estimating the six degrees of freedom pose of the object, without the need for the determination of neither the intrinsic nor the extrinsic parameters of the stereo vision system. The optimum design of the proposed DNN is obtained after comparing different activation functions and optimizers associated with the DNN.The proposed uncalibrated DNN-based method performance is compared to that of the traditional calibration-based method. In the calibration-based method, the rotational matrix relating the robot coordinates to the stereo vision coordinates is computed using two approaches. The first approach uses Singular Value Decomposition (SVD) while the second approach uses a novel proposed modification of particle swarm optimization (PSO) called Hyper particle Scouts optimization (HPSO). HPSO outperforms other metaheuristic optimization algorithms such as PSO and genetic algorithm (GA).Exhaustive tests are performed, and the proposed DNN-based method is shown to outperform all tested alternatives.

Video Steganography Based on Shi-Tomasi Corner Detection and Least Significant Bit Algorithm

Today, the digital and social media platforms are extremely trending, leading a demand to transmit knowledge very firmly. The information that is exchanged daily becomes ‘a victim’ to hackers. To beat this downside, one of the effective solutions is Steganography or Cryptography. In this paper, the video Steganography and cryptography thoughts are employed, where a key text is hidden behind a ‘certain frame’ of the video using Shi-Tomasi corner point detection and Least Significant Bit (LSB) algorithmic rule. Shi-Tomasi algorithmic rule is employed to observe, the corner points of the frame. In the proposed work, a ‘certain frame’ with large number of corner points is chosen from the video. Then, the secret text is embedded within the detected corner points using LSB algorithmic rule and transmitted. At the receiver end, decryption process is employed, in the reverser order of encryption to retrieve the secret data. As a technical contribution, the average variation of Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index are analysed for original and embedded frames and found to be 0.002, 0.016 and 0.0018 respectively.