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- W2908299474 abstract "The main goal of this paper is to develop an application using deep learning to enhance advanced driver assistance system (ADAS) features based on internal, external and environmental factors from a driver perspective (henceforth called ADASDL). It covers features like drowsiness detection, de-raining and traffic sign detection models. In this approach, we will analyse driver eye blinking to detect drowsiness of driver and also removing rain streaks from individual images based on the deep convolutional neural network (CNN). This proposed approach is implemented with deep learning models. The results including inference time are discussed." @default.
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- W2908299474 date "2018-12-31" @default.
- W2908299474 modified "2023-09-27" @default.
- W2908299474 title "ADASDL—Innovative Approach for ADAS Application Using Deep Learning" @default.
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- W2908299474 doi "https://doi.org/10.1007/978-981-13-3329-3_46" @default.
- W2908299474 hasPublicationYear "2018" @default.
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