Performance of Machine Learning for Lane Detection
K. Malathi1, R. Kavitha2, N. Rahul Varma3
1K. Malathi, Associate Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai India.
2R. Kavitha, Associate Professor, Department of Computer Science and Engineering, BIST, BIHER, Chennai, Tamilnadu, India
3N. Rahul Varma, UG Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai.
Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 187-192 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11280789S419/19©BEIESP | DOI: 10.35940/ijitee.I1128.0789S419
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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: With the approach of the self supervised cars, the significance and exactness of path discovery has accomplished principal significance in the field of perception and imaging. In this paper, we propose a calculation to accomplish path recognition on streets utilizing the real-time data accumulated by the camera and applying K-means clustering method to report data in a way reasonable to make a feasible guide. The proposed method utilizes the physical way of the data to group the data. Silhouette coefficient is utilized to decide the quantity of groups in which the data ought to be partitioned. Paths are added to get the right markings. We show the adequacy of, the proposed method utilizing real-time activity data to commotion, shadows, and light varieties in the caught street pictures, and its materialness to both stamped and unmarked streets
Keywords: Field of Perception, Real-Time Data, K-Means Clustering Method, Silhouette Coefficient
Scope of the Article: Machine Learning