Detecting Technology of Current Generator Blade Shape Based on Improved Canny Algorithm

Hu, Tengyan; Qi, Zhanfeng; Qin, Yvfeng; Shi, Jian

doi:10.1088/1757-899x/452/3/032026

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…The CV2 function provided by OpenCV, an open-source computer vision library, was used to count the number of pixels contained in the tree-crown of each of the 235 trees (Section “Field survey data”) based on the tree-crown mask image and calculate the crown projection area (CPA) based on the image resolution. Using Canny’s algorithm ( Hu et al, 2018 ) to extract the edge features of each crown of 235 trees, followed by the ellipse fitting algorithm ( Yan et al, 2008 ) to obtain each crown’s external ellipse. Calculate the long and short axes of the ellipse as the maximum and minimum values of C. oleifera crown width, and then calculate the mean of these two values to obtain the average crown width of C. oleifera ( Zhang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Yan

Yin

et al. 2022

Front. Plant Sci.

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As one of the four most important woody oil-tree in the world, Camellia oleifera has significant economic value. Rapid and accurate acquisition of C. oleifera tree-crown information is essential for enhancing the effectiveness of C. oleifera tree management and accurately predicting fruit yield. This study is the first of its kind to explore training the ResU-Net model with UAV (unmanned aerial vehicle) images containing elevation information for automatically detecting tree crowns and estimating crown width (CW) and crown projection area (CPA) to rapidly extract tree-crown information. A Phantom 4 RTK UAV was utilized to acquire high-resolution images of the research site. Using UAV imagery, the tree crown was manually delineated. ResU-Net model’s training dataset was compiled using six distinct band combinations of UAV imagery containing elevation information [RGB (red, green, and blue), RGB-CHM (canopy height model), RGB-DSM (digital surface model), EXG (excess green index), EXG-CHM, and EXG-DSM]. As a test set, images with UAV-based CW and CPA reference values were used to assess model performance. With the RGB-CHM combination, ResU-Net achieved superior performance. Individual tree-crown detection was remarkably accurate (Precision = 88.73%, Recall = 80.43%, and F1score = 84.68%). The estimated CW (R2 = 0.9271, RMSE = 0.1282 m, rRMSE = 6.47%) and CPA (R2 = 0.9498, RMSE = 0.2675 m2, rRMSE = 9.39%) values were highly correlated with the UAV-based reference values. The results demonstrate that the input image containing a CHM achieves more accurate crown delineation than an image containing a DSM. The accuracy and efficacy of ResU-Net in extracting C. oleifera tree-crown information have great potential for application in non-wood forests precision management.

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Section: Methodsmentioning

confidence: 99%

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Yan

Yin

et al. 2022

Front. Plant Sci.

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“…Meanwhile, gaussian-based or second-order approaches capture local maxima of the image gradient, the process is also named as zero-crossings of the Laplacian operator. To yield a better edge detection result, a smoothing process, typically Gaussian smoothing and anisotropic diffusion, will be done in pre-processing step [16] [17]. As reported by Iskandar, Gaussian filter kernel size is critically important to result in different levels of the blurred image.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the non-local means (NLM) filter is suggested to replace the Gaussian smoothing filter as it evaluates the similarity of the pixel with nearby pixels, taking into consideration not only their intensity difference but also from the whole spatial neighbourhood [1]. Another published framework has proven that the canny method could be further improved with an appropriate Gaussian filter for effective white noise reduction by extending its image gradient magnitude with the Sobel algorithm [16]. A threshold is normally applied after the computation of edge magnitude to decide whether an edge pixel is to be kept.…”

Section: Related Workmentioning

confidence: 99%

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Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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“…Its greatest advantages lie in its simplicity and effectiveness. The classical edge detection algorithms mainly include Roberts operator, Sobel operator, Prewitt operator, Canny operator, Kirsch operator, Laplacian operator and Laplacian-Gauss (LoG) operator [3][4][5][6]. Such methods can obtain fine single-pixel edges in simple scenes, but when there are background, lighting, other non-edge interference factors in the actual scene, edge detection often appears "overdetection", the effect is difficult to guarantee, and the robustness is not high.…”

Section: Introductionmentioning

confidence: 99%

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Wang¹,

Li²,

Wang³

et al. 2022

Computers, Materials &Amp; Continua

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Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.

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Detecting Technology of Current Generator Blade Shape Based on Improved Canny Algorithm

Cited by 4 publications

References 6 publications

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model

Mini Kirsch Edge Detection and Its Sharpening Effect

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

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