Abstract: The present disclosure discloses systems and method for real-time continuous lane mapping. The technique comprising receiving an input image of a road captured by an image sensor mounted on a vehicle; extracting one or more feature vectors from the image; extracting lane mark coefficients and lane type features from the one or more extracted feature vectors; detecting a lane mark by computing the coefficients and applying a pre-learned value; comparing the lane type relating to right and/or left lane markings with a predefined lane class; classifying the left and/or right lane markings based on the comparison and applying a pre-learned value; and generating a lane map along with the lane markings. The step of the lane detection and the classification is performed simultaneously based on the one or more extracted feature vectors.

Abstract: The present disclosure relates to a method for real-time object detection, the method comprising: capturing an image in vicinity of a vehicle; feeding the captured image to a deep fully convolution neural network; extracting one or more relevant features from the captured image; classifying the extracted features using one or more branches to identify different size of objects; predicting objects present in the image based on a predetermined confidence threshold; and marking the predicted objects in the image.

Abstract: The present disclosure relates to a method for real-time object detection, the method comprising: capturing an image in vicinity of a vehicle; feeding the captured image to a deep fully convolution neural network; extracting one or more relevant features from the captured image; classifying the extracted features using one or more branches to identify different size of objects; predicting objects present in the image based on a predetermined confidence threshold; and marking the predicted objects in the image.

Abstract: The present disclosure discloses systems and method for real-time continuous lane mapping. The technique comprising receiving an input image of a road captured by an image sensor mounted on a vehicle; extracting one or more feature vectors from the image; extracting lane mark coefficients and lane type features from the one or more extracted feature vectors; detecting a lane mark by computing the coefficients and applying a pre-learned value; comparing the lane type relating to right and/or left lane markings with a predefined lane class; classifying the left and/or right lane markings based on the comparison and applying a pre-learned value; and generating a lane map along with the lane markings. The step of the lane detection and the classification is performed simultaneously based on the one or more extracted feature vectors.