Open AccessMultilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network
Author(s) -
Suvarna Shirke,
R. Udayakumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1505.0882s819
Subject(s) - convolutional neural network , computer science , entropy (arrow of time) , artificial intelligence , cross entropy , path (computing) , segmentation , sensitivity (control systems) , pixel , deep learning , artificial neural network , focus (optics) , pattern recognition (psychology) , data mining , machine learning , engineering , physics , optics , quantum mechanics , electronic engineering , programming language
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