Research on Sobel Edge Detection Algorithm of Grayscale Images to Analyse Car Number Plate

• Published in: SinkrOn Vol. 9; no. 2; pp. 598 - 604
• Main Authors: Putra, Tri Dharma, Purnomo, Rakhmat
• Format: Journal Article
• Language: English
• Published: 14.04.2025
• ISSN: 2541-044X; 2541-2019; 2541-2019
• DOI: 10.33395/sinkron.v9i2.14538

Image processing is a very important subject to be discussed in computer science. Many applications of image processing are already in the field. Image processing techniques are applied in color and grayscale images. The application of image processing are ranging for military, medical and many other applications. One most important thing to analyse image and enhance its quality is doing edge detection. Edge detection in image is a well known approach to be used to detect discontinuity in grayscale image. Edge detection functions to identify edge line in images. Sobel algorithm is one of most known algorithm, others are prewitt, canny, homogeneity algorithms. Image can be made sharper and will enhance  its quality. To detect number plate of cars, an edge detection algorithm needs to be applied. In number plate, to recognize the cars number plate, the image should be clear and clean from dirt. Sometimes we can not recognize the plate number if it is too blur or has many dirt. So in its application we need a strong edge detection algorithm to recognize car number plate easily. In this journal, five car’s images are presented. Each with the original image, grayscale image and the image after edge detected by sobel algorithm. It is concluded that this algorithm is quiet good in the implementation. But in the result, there are poor quality image also. For PSNR of images after edge detected, their values are between 19 and 20 dB, which are not good.