Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
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Real time lane detection and tracking (LDT) is one of the most consequential parts to performing the above tasks.
We propose an end-to-end three-task convolutional neural network (3TCNN) having two regression branches of bounding boxes and Hu moments and one classification branch of object masks for lane detection and road recognition.
Animals have been a common sighting on roads in India which leads to several accidents between them and vehicles every year.
The lane number that the vehicle is traveling in is a key factor in intelligent vehicle fields.
3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection which is able to detect 3d lanes of any arbitrary topology such as splits, merges, as well as short and perpendicular lanes.
So we combine the advantages of spatial convolution in spatial information processing and the efficiency of ERFNet in semantic segmentation, propose an end-to-end network to lane detection in a variety of complex scenes.
The incorporation of an event camera for lane detection tasks in the perception stack of autonomous driving is one of the most promising solutions for mitigating challenges encountered by RGB cameras.