Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN for Lane Detection
Lane detection using Enhanced Manta-Ray Foraging Optimization Algorithm based DCNN
Keywords:
lane detection, deep learning, DCNN, bird's eye view image, optimal weightsAbstract
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.
References
Jigang TangSongbin LiPeng Liu,"A review of lane detection methods based on deep learning", Pattern Recognition, 15 September 2020.
Zhiyuan ZhaoQi WangXuelong Li,"Deep reinforcement learning based lane detection and localization", Neurocomputing, 9 July 2020. Enhanced Manta-Ray Foraging Optimization Algorithm Based DCNN For Lane Detection 41
K. DinakaranA. Stephen SagayarajGokul Chandrasekaran,"Advanced lane detection technique for structural highway based on computer vision algorithm", Materials Today: Proceedings Available online, 24 October 2020.
Raja MuthalaguAnudeepsekhar BolimeraV. Kalaichelvi,"Lane detection technique based on perspective transformation and histogram analysis for self-driving cars", Computers & Electrical Engineering, 18 May 2020.
Ge ZhangChaokun YanJianlin Wang,"Quality-guided lane detection by deeply modeling sophisticated traffic context", Signal Processing: Image Communication,19 February 2020.
Jingyi Liu,"Learning full-reference quality-guided discriminative gradient cues for lane detection based on neural networks",Journal of Visual Communication and Image Representation,9 October 2019.
Mallot, H.A., Bulthoff, H.H., Little, J.J and Bohrer, S. (1991). Inverse perspective mapping simplifies optical flow computation and obstacle detection, Biological cybernetics, 64(3): 177-185.
Rakhlin, A., Shvets, A., Iglovikov, V and Kalinin, A.A. "Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis, International Conference Image Analysis and Recognition", ICIAR, Image Analysis and Recognition, pp. 737-744, 2018.
Jiaying FengXiaoguang LuoSomayeh Pouramini,"Minimization of energy consumption by building shape optimization using an improved Manta-Ray Foraging Optimization algorithm", Energy Reports, 15 February 2021.
K.Srinivas,"Prediction of E-Learning Efficiency by Deep Learning in EKhool Online Portal Networks",
Multimedia Research, vol. 3, no. 4, October 2020
Arvind Madhukar Jagtap,"Developing Deep Neural Network for Learner Performance Prediction in EKhool Online Learning Platform",Multimedia Research, vol. 3, no. 4, October 2020.
G.Gokulkumari,"Classification of Brain Tumor using Manta Ray Foraging Optimization-based Deep CNN Classifier",Multimedia Research, vol. 3, no 4, October 2020.
