Hybrid optimisation dependent deep belief network for lane detection

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.

KEYWORDS:

• Driver assistance system
• lane detection model
• bird’s eye view image
• image transformation
• optimization algorithm

Nomenclature

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Disclosure statement

No potential conflict of interest was reported by the authors.