Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network
Suvarna Shirke1, R. Udayakumar2

1Suvarna Shirke, Research Scholar, Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Dr. R. Udayakumar, Professor and Supervisor, Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1967-1974 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B15050882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1505.0882S819
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Abstract: In today’s world, the conditions of road is drastically improved as compared with past decade. Most of the express highways are made up of cement concrete and equipped with increased lane size. Apparently speed of the vehicle will increase. Therefore there are more chances for accidents. To avoid the accidents in recent days driver assistance systems are designed to detect the various lane. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. In this paper the entropy based fusion approach is presents for detecting multi-lanes. The Earth Worm- Crow Search Algorithm (EW-CSA) which is based on Deep Convolution Neural Network(DCNN) is utilized for consolidating the outcomes. At first, the deep learning approaches for path location is prepared using an optimization algorithm and EW-CSA, which focus on characterizing every pixel accurately and require post preparing activities to surmise path data. Correspondingly, the region based segmentation approach is utilizing for the multi-lane detection. An entropy based fusion model is used because this method preserved all the information in the image and reduces the noise effects. The performance of proposed model is analyzed in terms of accuracy, sensitivity, and specificity, providing superior results with values 0.991, 0.992, and 0.887, respectively.
Keywords: Segmentation, Multilane Classification, Multilane Detection, Deep Convolution Neural Network (DCNN), Earth Worm- Crow Search Algorithm (EW-CSA), Driver Assistance System (DAS).
Scope of the Article: Deep Learning