An adaptive ant colony optimisation for improved lane detection in intelligent automobile vehicles
by Ahmed Tijani Salawudeen; Ime Jarlath Umoh; Bashir Olaniyi Sadiq; Olubukola Ishola Oyenike; Muhammed Bashir Mu'azu
International Journal of Bio-Inspired Computation (IJBIC), Vol. 19, No. 2, 2022

Abstract: This paper presents an improved lane detection algorithm using an adaptive ant colony optimisation (aACO) based edge detection technique. In the paper, we first modified the ACO to select its control parameters (pheromone influencer, heuristic influencer, evaporation rate and the pheromone decay coefficient) adaptively. The modified ACO was first used to solve some selected CEC benchmark functions of diverse properties. Thereafter, the algorithm was used to implement an edge detection algorithm for lane detection. To this, a threshold tolerance technique was developed to determine the element of the final ant pheromone matrix that constitutes a lane edge. Three lane detection testbeds (traffic, liquid and cloudy) were created to evaluate the performance of the develop algorithm. Simulations were performed using MATLAB and results shows that the dynamic parameter-based lane detection can detect lane line better than the Canny edge detector, irrespective of any occlusion in the lane images.

Online publication date: Tue, 01-Mar-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com