Abstract
One of the key technologies in autonomous vehicles is image based lane detection algorithm. High performance is detected in modern deep learning methods. But in case of challenging areas like congested roads or poor lighting conditions, it is difficult to accurately detect lanes. Global context information is required which can be extracted from limited visual-cue. Moreover, for automotive driver assisting system, like lane keep, collision avoid etc., it is important to know the position of the vehicle i.e., in which lane it is. Due to large varieties in shape and colour of the lane marking, it becomes difficult to solve this task. For this purpose, an initial step on the input image is the image processing, where the data is processed as per the requirement in pixel level semantic segmentation. Then comes in the creation of the semantic segmentation model which is able to process the data. This model can be of different variant based on the computation ability, as well as the parameter handling capacity.
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Rajalakshmi, T.S., Senthilnathan, R. (2022). A Study on Different Types of Convolutions in Deep Learning in the Area of Lane Detection. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_8
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