Adaptive Edge Detection Algorithm Research Articles

Layer Contour Geometric Characterization in MEX/P through CIS-Based Adaptive Edge Detection

The industrial adoption of material extrusion of polymers (MEX/P) is hindered by the geometric quality of manufactured parts. Contact image sensors (CISs), commonly used in flatbed scanners, have been proposed as a suitable technology for layer-wise characterization of contour deviations, paving the way for the application of corrective measures. Nevertheless, despite the high resolution of CIS digital images, the accurate characterization of layer contours in MEX/P is affected by contrast patterns between the layer and the background. Conventional edge-recognition algorithms struggle to comprehensively characterize layer contours, thereby diminishing the reliability of deviation measurements. In this work, we introduce a novel approach to precisely locate contour points in the context of MEX/P based on evaluating the similarity between the grayscale pattern near a particular tentative contour point and a previously defined gradient reference pattern. Initially, contrast patterns corresponding to various contour orientations and layer-to-background distances are captured. Subsequently, contour points are identified and located in the images, with coordinate measuring machine (CMM) verification serving as a ground truth. This information is then utilized by an adaptive edge-detection algorithm (AEDA) designed to identify boundaries in manufactured layers. The proposed method has been evaluated on test targets produced through MEX/P. The results indicate that the average deviation of point position compared to that achievable with a CMM in a metrology laboratory ranges from 8.02 µm to 13.11 µm within the experimental limits. This is a substantial improvement in the reliability of contour reconstruction when compared to previous research, and it could be crucial for implementing routines for the automated detection and correction of geometric deviations in AM parts.

Corn Row Navigation Line Extraction Method Based on the Adaptive Edge Detection Algorithm

Aiming at the problems of long detection time and large detection error when agricultural machinery extracts corn row navigation lines, a method of corn row navigation line extraction based on the adaptive edge detection algorithm is proposed. First, the improved super-green feature algorithm and the maximum inter-class variance method are used to automatically obtain the green feature binary image, and the morphological processing is used to improve the image quality, determining dynamic regions of interest by constraining pixel thresholds, extraction of corn edge contour using adaptive edge detection algorithm, finally, the feature points are fitted by the Theil-Sen estimation method. Experimental results show: the super-green feature algorithm reflects the green content in the image more realistically, using the adaptive edge detection algorithm to extract corn row features, the accuracy rate is 94%, and the processing time of a single frame image is 104ms. Compared with the Hough algorithm extraction and the vertical projection algorithm, the navigation line extraction accuracy is increased by 15% and 8% respectively, and the time-consuming is reduced by 258ms and 150ms respectively. In addition, the stability of the algorithm is analyzed in different environments, all with good timeliness.

An APF-ACO algorithm for automatic defect detection on vehicle paint

As a popular technology in the field of artificial intelligence, computer vision is gradually adapting to the needs of convenience for human beings, improving production efficiency and reducing production costs. Therefore, this study proposes a computer vision algorithm to locate and identify the location of defects. For the traditional edge detection algorithm Sobel, LoG, Canny, the decisive factor for the detection effect of paint defect image is the adjustment of parameters, which can’t achieve an adaptive edge detection algorithm for paint defects, so it is thought that the evolution idea of ant colony algorithm can be used to achieve accurate detection of defects. This paper proposes an automatic detection method for vehicle body paint film defects based on computer vision. An ant colony optimization edge detection algorithm based on automotive paint features (APF-ACO) is proposed. By combining global update and local update, the convergence speed of ant colony algorithm is improved and a new pheromone calculation and update method is proposed to effectively preserve the edge details of the detected image. A reflection area detection algorithm based on HSV color space is designed to detect the reflective area and eliminate interference. Establish defect classification identification rules, identify and mark five types of defects, and determine defect categories. Experiments show that the method can effectively detect the defect area and the recognition accuracy is 97.76%.

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3.

To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.

Automatic barcode recognition method based on adaptive edge detection and a mapping model

An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. AEDM is an edge detection-based method that has three consecutive phases. The first phase extracts the scan lines from a cropped image. The second phase involves detecting the edge points in a scan line. The edge positions are assumed to be the intersecting points between a scan line and a corresponding well-designed reference line. The third phase involves adjusting the preliminary edge positions to more reasonable positions by employing prior information of the coding rules. Thus, a universal edge mapping model is established to obtain the coding positions of each edge in this phase, followed by a decoding procedure. The Levenberg–Marquardt method is utilized to solve this nonlinear model. The computational complexity and convergence analysis of AEDM are also provided. Several experiments were implemented to evaluate the performance of AEDM algorithm. The results indicate that the efficient AEDM algorithm outperforms state-of-the-art methods and adequately addresses multiple issues, such as out-of-focus blur, nonlinear distortion, noise, nonlinear optical illumination, and situations that involve the combinations of these issues.

Lunar surface crater topology generation using adaptive edge detection algorithm

The probability of precise soft landing on the lunar surface could be significantly increased by using craters as a navigational landmark. This article aims at the edge information of crater for lander navigation. First, histogram of an image is calculated and adaptive Gaussian filter is applied. Then adaptive canny algorithm is applied to detect edges of craters at different illumination. The craters are extracted by the mechanism of paring the selected valid edges followed by pattern fitting. These detected craters are used as navigational landmarks and for velocity estimation of the lander. Experimental results show the adaptive ability of algorithms for crater detection at different illuminations and different terrains. Craters at high illuminations are also efficiently detected. These detected craters are matched with the reference crater database by use of pattern matching algorithm for horizontal velocity calculations.

Research on Broken Filament Detection Method of Silk Product

In this paper, we put forward a kind of adaptive edge detection algorithm of Gabor filter for silk product broken filament image. Use different directions’ Gabor filter to respectively get the broken filament image edge information. Using the method proposed in this paper to fuse the edges adaptively obtained from different directions Gabor filter, we obtain ideal image edges, and effectively eliminate the noise, also enhance the fuzzy edges at the same time. Experimental results show that the algorithm for silk products processing is effective, and the broken filament detected is clear.

一种改进的Sobel自适应边缘检测的FPGA实现

The paper proposes an improved Sobel adaptive edge detection algorithm for the sensitivity to noise and given threshold of the classical Sobel algorithm.First,the classical Sobel algorithm is improved and the direction of the detection is expanded.The adaptive threshold is automatically generated according to the background gray of the detecting pixel and the human visual property. Accordingly,the image edge that is more consistent with human subjective vision can be detected. The improved algorithm restrains effectively the noise by processing the edge image with mathematical morphologic method.The improved edge detection algorithm is embedded in a single FPGA and the system can collect image with high frame and resolution,detect edge and then display in real time taking advantage of high-speed parallel processing of FPGA.

Multi-Layer and Adaptive Edge Detection Method Based on Multi-Scale Gabor Wavelets

Recent years, the common adaptive edge detection algorithms use merely the global character of images, and the local character is ignored. The lack of image information always leads these algorithms are difficult to detect the low contrast edges and sensitive to asymmetric illumination. A novel adaptive edge detection algorithm is proposed to improve the edge detection performance. First, the original image is filtered to obtain the edge response image by introducing multi-scale Gabor filters. Second, non-maxima suppression technique is used on the edge response image to get the coarse edge points. Then, the edge response image is delaminated to adaptively select different thresholds towards different pixels of different layers according to pixels local mean and variance values. The fine edge points are got after filtering the edge response image with these thresholds. Finally, combine the coarse edge points and fine edge points together and get the final edge. Experimental results show that the proposed algorithm achieves lower contrast edge detection and robustness to asymmetric illumination, has better edge detection performance.

Edge Detection of the Impulse Noise Pollution Image Based on the Rough Set

The rough set is an effective tool to deal with incomplete, uncertain and fuzzy problem. In order to extract the edges information of the image by impulse noise pollution, further service for the follow-up image analysis and understanding, on the basis of the impulse noise pollution image preprocessing, an adaptive edge detection algorithm is put forward to process the impulse pollution image using rough set theory in this paper. Considering the target edge features can form a series of edge point constraints as the algorithm foundation,this method starts from the characteristics difference of the image edge and target area. Firstly, series edge constraint points are defined, and then the related questions of these conditions are solved by using rough set theory, so the edge detection algorithm is established. The experimental results show that this algorithm can effectively extract edge from the noise image information, to overcome the sensitivity of traditional algorithm for noise.