Real-Time Ego-Lane Detection in a Low-Cost Embedded Platform using CUDA-Based Implementation
نویسندگان
چکیده
This work assesses the effectiveness of heterogeneous computing based on a CUDA implementation for real-time ego-lane detection using typical low-cost embedded computer. We propose and evaluate CUDA-optimized algorithm approach extraction features from an aerial perspective image. The method incorporates well-known algorithms optimized to achieve very efficient solution with high rates combines techniques enhance markings remove noise. CUDA-based is compared OpenCV library serial CPU implementation. Practical experiments TuSimple's image datasets were conducted in NVIDIA's Jetson Nano detects up 97.9% ego lanes accuracy 99.0% best-evaluated scenario. Furthermore, performs at greater than 300 fps system, speeding 25 140 times implementations same platform, respectively. These results show that more complex solutions can be employed better while maintaining requirements low-power computer
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ژورنال
عنوان ژورنال: Semina
سال: 2023
ISSN: ['1676-5435', '1679-0367']
DOI: https://doi.org/10.5433/1679-0375.2023.v44.48268