Abstract: The present disclosure relates to a method and apparatus for generating training data of a deep learning model for lane classification. The method according to an embodiment of the present disclosure is performed by an electronic apparatus, and is a method for generating training data of a deep learning model for lane classification by generating a composite image of the other color lane using images of a white lane and the other color lane, and includes determining a ratio of other two channels based on one channel (reference color channel) for three color channels of red (R), green (G) and blue (B) of the other color lane in the image of the other color lane; and generating a composite image of the other color lane by scaling the image of the white lane by applying the determined ratio to the other two channels with respect to the reference color channel of the white lane.

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 is related in general to field of machine learning and image processing, that provides a method and system for detecting lane pattern. A lane classification system receives an input image comprising lane markings and co-ordinates of the lane markings, which is divided into two portions, a first image portion and a second image portion based on the lane markings and the co-ordinates of the lane markings. Further, the first image portion and the second image portion are resized into a predefined template size. The lane classification system detects a lane pattern in the two portions by analysing each of the one or more lane markings located in the resized first image portion and the second image portion, independently, based on historical lane data.

Abstract: A device for determining lane type and method thereof are provided. The device for determining lane type according to an embodiment of the present disclosure includes a camera for acquiring an around view image around a vehicle, a GPS receiver for receiving GPS information, and a controller communicatively connected to the camera and the GPS receiver. Here, the controller is configured to recognize a scene of the image acquired by the camera, detect lanes and road markings from the recognized scene, comprise a classifier adapted based on the GPS information, classify the detected lanes and road markings by the classifier, and confirm a type of the classified lanes.

Abstract: Disclosed are a pedestrian detection method of a vehicle and a pedestrian detection system thereof. The pedestrian detection method includes transmitting and receiving a radar signal to and from an object in front of a vehicle to sense the object; capturing an exterior image of the vehicle; calculating a distance between the vehicle and the sensed object, detecting the sensed object in the captured image, and setting a region of interest (ROI) including the sensed object; and detecting a pedestrian in the ROI.

Abstract: Disclosed are a pedestrian detection method of a vehicle and a pedestrian detection system thereof. The pedestrian detection method includes transmitting and receiving a radar signal to and from an object in front of a vehicle to sense the object; capturing an exterior image of the vehicle; calculating a distance between the vehicle and the sensed object, detecting the sensed object in the captured image, and setting a region of interest (ROI) including the sensed object; and detecting a pedestrian in the ROI.