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- W3003260967 abstract "Visual driving scene perception systems have been gained popularity among the autonomous driving research community following the advent of deep learning technology. Moreover, the multi-task deep learning model has been an important tool with respect to unifying the tasks performed in a driving scene perception system, such as scene classification, object detection, segmentation, depth estimation. In this paper, we introduce our developed multi-task deep-learning model design and training tool, for unified road scene perception model. Additionally, we also propose a sequential auxiliary multi-task training method that can train a multi-task model, using different datasets for each tasks. Finally, we present a unified road segmentation and depth estimation model, based on multi-task deep learning, to verify the efficiency and feasibility of our developed tool. Experimental results for KITTI datasets show that our tool-based unified road segmentation and depth estimation model can successfully segment the driving road and estimate its depth." @default.
- W3003260967 created "2020-02-07" @default.
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- W3003260967 date "2019-10-01" @default.
- W3003260967 modified "2023-09-24" @default.
- W3003260967 title "Multi-Task Deep Learning Design and Training Tool for Unified Visual Driving Scene Understanding" @default.
- W3003260967 cites W1686810756 @default.
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- W3003260967 doi "https://doi.org/10.23919/iccas47443.2019.8971526" @default.
- W3003260967 hasPublicationYear "2019" @default.
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