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Hybrid optimisation dependent deep belief network for lane detection
Journal of Experimental & Theoretical Artificial Intelligence ( IF 2.2 ) Pub Date : 2020-12-15 , DOI: 10.1080/0952813x.2020.1853249
Suvarna Shirke 1 , R. Udayakumar 2
Affiliation  

ABSTRACT

Nowadays, in research introducing an advanced driver assistance system for improving driving is considered as the trending one. In this research, concentrate more on proposing lane detection model and assist in driving. This work develops a lane detection model through the deep learning scheme. The proposed scheme has two major phases, such as image transformation and lane detection. Initially, the proposed method obtains the multiple-lane images and transforms the image and this image transformation helps in classifier training. For detecting the lane from the bird’s view image, this work considers the Deep Convolution Neural Network (DCNN) classifier. A novel optimisation algorithm, namely Earth Worm-Crow Search Algorithm (EW-CSA), is developed in this work to assist the DCNN classifier with the optimal weights. The proposed algorithm is developed by modifying the Earth Worm Optimisation algorithm (EWA) with the properties of the Crow Search Algorithm (CSA). The proposed system is compared with other existing methods, in which the proposed method offers maximum sensitivity 0.9925, the detection accuracy of 0.99512, and specificity of 0.995.



中文翻译:

用于车道检测的混合优化依赖深度置信网络

摘要

如今,在研究中,引入先进的驾驶辅助系统来改善驾驶被认为是趋势之一。在这项研究中,更多地集中在提出车道检测模型和辅助驾驶。这项工作通过深度学习方案开发了车道检测模型。所提出的方案有两个主要阶段,例如图像转换和车道检测。最初,所提出的方法获得多车道图像并对图像进行变换,这种图像变换有助于分类器训练。为了从鸟瞰图像中检测车道,这项工作考虑了深度卷积神经网络(DCNN)分类器。在这项工作中开发了一种新的优化算法,即地球蠕虫-乌鸦搜索算法(EW-CSA),以帮助 DCNN 分类器获得最佳权重。所提出的算法是通过使用乌鸦搜索算法 (CSA) 的属性修改地球蠕虫优化算法 (EWA) 来开发的。将所提出的系统与其他现有方法进行比较,其中所提出的方法的最大灵敏度为 0.9925,检测精度为 0.99512,特异性为 0.995。

更新日期:2020-12-15
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