Speed Breaker Detection Using GLCM Features
M.Bharathi1, A.Amsaveni2, B. Manikandan3
1M. Bharathi, Professor, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore (TamilNadu), India.
2A.Amsaveni, Professor, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore (TamilNadu), India.
3Mani kandan.B, PG Scholar, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore (TamilNadu), India.
Manuscript received on 10 December 2018 | Revised Manuscript received on 17 December 2018 | Manuscript Published on 30 December 2018 | PP: 384-389 | Volume-8 Issue- 2S December 2018 | Retrieval Number: BS2672128218/19©BEIESP
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© 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: Road accidents are increasing worldwide, that leads to death, injuries and vehicle damages. Most of the accidents happen due to the improper warning sign and unnoticeable speed breakers on the road especially during night. Identification and notification of road signs and speed breakers to the driver at proper time is very important to avoid accidents. In this paper, speed breaker identification using Gray Level Co-occurrence Matrix (GLCM) features is proposed. This method has three stages namely pre-processing, feature extraction and classification. Noise removal, Resizing the image and gray scale conversion has been done as a part of pre-processing. In the feature extraction step, the spatial relationship between the pixels is obtained. GLCM features are the second order statistical features of the image. These features includes correlation, Angular Second Moment, Entropy, Homogeneity and contrast. In this paper, features are consider as the shape, texture and feature statistics. Neural Network based classifier is used in the third stage to identify the presence of speed breaker. The performance of the classifier is evaluated by calculating the confusion matrix.
Keywords: Speed Breaker, Image Processing, GLCM, Feature Extraction.
Scope of the Article: Communication