Comparative Analysis of Edge Detection Methods using Deep Learning
Comparative Analysis of Edge Detection Methods using Deep Learning |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-2 |
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| Year of Publication : 2023 | ||
| Author : Dipmala Salunke, Bhushan Dhamankar, Sairaj Chidrawar, Rohit Kangule, Shrikrushnakumar Sondge, Sumit Deshmukh |
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| DOI : 10.14445/22315381/IJETT-V71I2P208 | ||
How to Cite?
Dipmala Salunke, Bhushan Dhamankar, Sairaj Chidrawar, Rohit Kangule, Shrikrushnakumar Sondge, Sumit Deshmukh, "Comparative Analysis of Edge Detection Methods using Deep Learning," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 61-70, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P208
Abstract
Computer vision is a subset of artificial intelligence (AI), which is used to extract meaningful data from images.
It provides different features such as object detection, edge detection, image classification etc. Edge detection is very useful
in industries like the civil industry, agriculture industry, autonomous vehicles, facial recognition, manufacturing, etc.
Using opencv, we can use different edge detection operators to get object edges and detect objects. The main problem with
dimension detection is the edges. Edges are one of the important characteristics of the image, which can provide us with
very useful information about the object. Though edge detection is a very old topic, there is still no solid study to explain
which edge detection method will work best for dimension detection. So here is a comparative analysis to find which edge
detection algorithm performs best for dimension detection to locate excellent edges that will generate decent contours. All
edge detection systems' effectiveness needs to be evaluated. The edges of the image may be extracted using a variety of
edge detection algorithms, and the performance can be judged using metrics like signal-to-noise ratio (SNR), structural
similarity index measure (SSIM), entropy, peak signal-to-noise ratio (PSNR), mean squared error (MSE). In this paper, in
addition to first derivative operators like sobel, robert, and prewitt, gaussian-based algorithms, the laplacian of gaussian,
and the canny edge detector have also been taken into consideration. It is experimentally observed that the sobel operator
is performing better than others, with an average SNR value of 1.1730.
Keywords
Opencv, Edge detection, Dimension detection, Image segmentation, Prewitt, Sobel, Laplacian of
gaussian, Canny, Robert.
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