MTSAN: Multi-Task Semantic Attention Network for ADAS Applications
نویسندگان
چکیده
This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). proposes Module (SAM) that introduces contextual clues from subnet guide subnet. The SAM significantly boosts up performance and computational cost by considerably decreasing false alarm rate it is completely independent any other parameters. experimental results show effectiveness each component network demonstrate proposed MTSAN yields better balance between accuracy speed. Following post-processing methods, module tested proved for its in Lane Departure Warning System (LDWS) Forward Collision (FCWS). In addition, deployable on low-power embedded devices meet requirements yielding 10FPS @ 512 X 256 NVIDIA Jetson Xavier 15FPS Texas Instrument's TDA2x.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3068991