A Novel Method for Vehicle Detection using Edge Detection and Fuzzy Logic Based Algorithm
M. Jansirani1, P. Sumitra2

1Mrs. M. Jansirani, Ph.D Scholar, Department of Computer Science, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam (Tiruchengode)–637205. India.
2Dr. P. Sumitra, Professor, Department of Computer Science, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam (Tiruchengode)–637205. India.

Manuscript received on 23 January 2017 | Revised Manuscript received on 30 January 2017 | Manuscript published on 30 January 2017 | PP: 4-6 | Volume-5 Issue-6, January 2017 | Retrieval Number: F1644015617©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Vehicles moving on road are of importance because problems like traffic congestion, economic waste, jamming on the underpasses and over-bridges (if the vehicle passing through is not of the permissible size) are associated with them. These problems can be dealt with by using various morphological processes based image processing techniques to detect the vehicles. In this thesis, the images of moving and still vehicles have been taken and an algorithm is used for vehicle detection which is based on image processing techniques and classification of vehicles in the form of natural description based on fuzzy logic such as classification based on area and circumference using Fuzzy Logic. To perform classification, fuzzification of area and circumference is done and each vehicle type (e.g. small, medium and big) is assigned a measurement range of values by designing fuzzy rules and finally defuzzification is done. Edge detection is considered to be fundamental step in the field of image processing and computer vision. There are 3 types of discontinuities in a digital image: point, line, edge. The most common way is to use spatial masks which have properties to detect these discontinuities. More than isolated points and lines detecting edges are important because they form an important part of image segmentation. Edge detection is basically a method of segmenting an image into regions based on discontinuity, enhancing the presence of these discontinuities in the image allows us to improve the perceived image quality under certain conditions. Edge detection makes use of differential operators to detect changes in the gradients of the grey or color levels in the image. Edge detection is divided into two main categories: firstorder edge detection, example for first order edge detection are Sobel, Robert, Perwitt and second-order edge detection, example for second order edge detection are Laplacian and Canny. Image edge is often buried by noise, so it‘s necessary to research edge detection algorithm. Since traditional edge detection like Sobel, Perwitt, Robert operator are sensitive noise, to overcome that problem, some new algorithm is applied in edge detection such as Canny, Morphology, Neural network and Fuzzy logic. This is to be implemented in MATLAB. Fuzzy logic is one of the new methods and it was based on set theory. Fuzzy logic based algorithm is very efficient and flexible to detect the edges of vehicle in an input image by scanning it through the 2*2 mask. The main benefit of fuzzy set theory is able to model the ambiguity and the uncertainty.
Keyword: Fuzzy Logic, MATLAB., Neural network and Fuzzy logic

Scope of the Article: Fuzzy logic