Every year in India, most of the car accidents are occurs and affects on number of lives. Most of the road accidents are occurs due to driver’s inattention and fatigue. Drivers require to focus on different circumstances, together with vehicle speed and path, the separation between vehicles, passing vehicles, and potential risky or uncommon events ahead. Also the accident occurs due to the who bring into play cell phones at the same time as driving, drink and drive, etc. Due to this, most of the companies of automobiles tries to make available best Advanced Driver Assistance System (ADAS) to the customer to avoid the accidents. The lane detection approach is one of the method provided by automobile companies in ADAS, in which the vehicle must follows the lane. Therefore, there is less chance to get an accident. The information obtained from the lane is used to alert the driver. Therefore most of the researchers are attracted towards this field. But, due to the varying road circumstances, it is very difficult to detect the lane. The computer apparition and machine learning approaches are presents in most of the articles. In this article, we presents the deep learning scheme for identification of lane. There are two phases are presents in this work. In a first phase the image transformation is done and in second phase lane detection is occurred. At first, the proposed model gets the numerous lane pictures and changes the picture into its relating Bird's eye view picture by using Inverse perspective mapping transformation. The Deep Convolutional Neural Network (DCNN) classifier to identify the lane from the bird’s eye view image. The Earth Worm- Crow Search Algorithm (EW-CSA) is designed to help DCNN with the optimal weights. The DCNN classifier gets trained with the view picture from the bird’s eye image and the optimal weights are selected through newly developed EW-CSA algorithm. All these algorithms are performed in MATLAB. The simulation results shows that the exact detection of lane of road. Also, the accuracy, sensitivity, and specificity are calculated and its values are 0.99512, 0.9925, and 0.995 respectively.
(DCNN) Deep Convolution Neural Network Classifier and (EW-CSA) Earth Worm-Crow Search Algorithm for Lane Detection