Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN for Lane Detection

Lane detection using Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN


  • Snehal S. Shinde JSPM NTC


lane detection, deep learning, DCNN, bird's eye view image, optimal weights


In recent days, a developed driver support system has been introduced to enhance driving, which is considered as the renowned one. In the Advanced Driver assistive systems, Lane departure detection plays an important role and it enhances the vehicle's active safe driving. Nowadays, a decent lane detection model that is on the basis of the computer vision methods, is introduced in many studies. From a stream of videos, the lane boundaries and its radius of curvatures and lane direction is detected. This video footage is recorded from a camera mounted on the top of a vehicle. Here, the lane detection model is proposed, which concentrates more on driving assistance. In this paper, a deep learning scheme is used for a lane detection model. Here, the adopted strategies possess two main components namely lane detection and image transformation. At first, the multiple lane images are obtained by the adopted technique and it transforms the image, and it aids in classifying the training. The Deep Convolution Neural Network (DCNN) classifier is considered to detect the lane from the bird's eye view image. An Enhanced Manta-Ray Foraging Optimization Algorithm (EMRFOA) is proposed to aid the DCNN classifier with the optimal weights in this paper. Finally, the developed model is analyzed with the conventional models in that the performance of the adopted model is higher than the conventional models.


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How to Cite

Snehal S. Shinde. (2021). Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN for Lane Detection: Lane detection using Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN. Multimedia Research, 4(3). Retrieved from https://publisher.resbee.org/admin/index.php/mr/article/view/48