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Real-Time Multi-Task ADAS Implementation on Reconfigurable Heterogeneous mpsoc architecture

IEEE ACCESS 2023年 11卷 80741-80760页

作者： Tatar, Guner Bayar, Salih Fatih Sultan Mehmet Vakif Univ Dept Elect Elect Engn TR-34445 Istanbul Turkiye Marmara Univ Dept Elect & Elect Engn TR-34840 Istanbul Turkiye

The rapid adoption of Advanced Driver Assistance Systems (ADAS) in modern vehicles, aiming to elevate driving safety and experience, necessitates the real-time processing of high-definition video data. This requirement brings about considerable computational complexity and memory demands, highlighting a critical research void for a design integrating high FPS throughput with optimal Mean Average Precision (mAP) and Mean Intersection over Union (mIoU). Performance improvement at lower costs, multi-tasking ability on a single hardware platform, and flawless incorporation into memory-constrained devices are also essential for boosting ADAS performance. Addressing these challenges, this study proposes an ADAS multi-task learning hardware-software co-design approach underpinned by the Kria KV260 Multi-Processor System-on-Chip Field Programmable Gate Array (mpsoc-fpga) platform. The approach facilitates efficient real-time execution of deep learning algorithms specific to ADAS applications. Utilizing the BDD100K+Waymo, KITTI, and CityScapes datasets, our ADAS multi-task learning system endeavours to provide accurate and efficient multi-object detection, segmentation, and lane and drivable area detection in road images. The system deploys a segmentation-based object detection strategy, using a ResNet-18 backbone encoder and a Single Shot Detector architecture, coupled with quantization-aware training to augment inference performance without compromising accuracy. The ADAS multi-task learning offers customization options for various ADAS applications and can be further optimized for increased precision and reduced memory usage. Experimental results showcase the system's capability to perform real-time multi-class object detection, segmentation, line detection, and drivable area detection on road images at approximately 25.4 FPS using a 1920 x 1080p Full HD camera. Impressively, the quantized model has demonstrated a 51% mAP for object detection, 56.62% mIoU for image segmen

关键词： ADAS deep learning deep processing unit memory allocation multi-task learning mpsoc-fpga architecture Vitis-AI quantization aware training

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