With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.
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W pracy przedstawiono przegląd najskuteczniejszych metod detekcji (wykrywania) krawędzi obiektów w obrazach kolorowych. Zaprezentowano skuteczność wektorowych operatorów porządkowych i wektorowych operatorów różnicowych oraz ich odmiany rozszerzone o różnego typu filtry. Detektory uzyskane w ten sposób są w różnym stopniu skomplikowane obliczeniowo oraz odporne na szum impulsowy i gaussowski. W przypadku operatorów DV dodanie członów filtrujących istotnie podnosi ich odporność na obecność szumu. Najlepsze wyniki ekstrakcji krawędzi w zaszumionych obrazach kolorowych uzyskano, stosując operator DV_&aL-trim.
EN
The paper offers review of most effective methods of detecting edges in noised colour images. Presented has been effectiveness of vector order operators and vector difference operators, as well as variants thereof enriched with filters of different types. Detectors gained in this way are complicated to different degrees from the viewpoint of computations, and resistant to impulsive and Gaussian noises. In the case of DV operators, addition of filtering elements significantly increases resistance thereof to the presence of noise. Best effects of edge extraction in noised images have been gained using the DV_ Alpha-trim operator.
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