| Summary: | We presented a solution for lane recognition in rainy weather conditions in our
study that incorporates a self-attention module into an ENet model architecture. We
concentrate on specific weather conditions, such as rain, because they have received less
attention as a result of the numerous obstacles they present. One of the primary reasons
is that rain water distorts road lines, resulting in the appearance of distorted lanes and
markings. We trained our model on annotated labelled data obtained from the author of
VPGNet, which comprises a variety of situations, including ones in rainy conditions. We
presented a deep learning technique in our ENet architecture, utilizing ENet semantic
segmentation and a self-attention module. Our model can recognize and classify a given
image on a pixel-by-pixel basis, with each pixel representing a distinct class. We
developed a robust model that is capable of performing effectively under a variety of
real-world scenarios while maintaining a frame rate of 20 frames per second. Our testing
findings indicate that our technique reaches a level of accuracy comparable to that of the
fundamental ENet architectural model.
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