Real-Time Dimension Detection using Customized Canny Edge Detection Algorithm

Real-Time Dimension Detection using Customized Canny Edge Detection Algorithm
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-9
Year of Publication : 2023
Author : Dipmala Salunke, Pallavi Tekade, Nihar Ranjan, Deepali Ujalambkar, Sunil Sangve, Deepak Mane
DOI : 10.14445/22315381/IJETT-V71I9P233

How to Cite?

Dipmala Salunke, Pallavi Tekade, Nihar Ranjan, Deepali Ujalambkar, Sunil Sangve, Deepak Mane, "Real-Time Dimension Detection using Customized Canny Edge Detection Algorithm," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 375-384, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P233

Abstract
Computer vision is a subset of Artificial Intelligence utilized to extract informative data from images. It offers a variety of features, including image categorization, edge detection, and object identification. Edge detection is particularly helpful in a variety of fields, including construction, agriculture, manufacturing, autonomous cars, and facial recognition. The system can obtain object edges and determine an object's dimensions by applying various edge detection operators while using opencv. The edges are the fundamental issue with dimension detection. One of the key aspects of the image that might provide us with highly helpful information about an object is its edges. Despite the fact that edge detection is an extremely ancient subject, no thorough research has been done to clarify which edge detection technique will work the best for dimension recognition. The paper introduces a Customized Canny Edge Detection Algorithm (CCEDA) for real-time object processing, eliminating the need for a dataset. To improve the performance of the canny edge detector, the operator combines bilateral filtering with morphological operations like dilation and erosion. Metrics including Signal Noise Ratio (SNR), Structural Similarity Index Measure (SSIM), entropy, Peak Signal to Noise Ratio (PSNR), and Mean Squared Error (MSE) are used to evaluate the modified canny edge detector's performance. The accuracy for dimension detection is reported to be 92.01%.

Keywords
Edge detection, Dimension detection, Image segmentation, Canny edge detector.

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