Chi Tsun-Kuang, Chen Tsung-Yi, Lin Yu-Chen, Lin Ting-Lan, Zhang Jun-Ting, Lu Cheng-Lin, Chen Shih-Lun, Li Kuo-Chen, Abu Patricia Angela R
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan.
Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan.
Sensors (Basel). 2024 May 13;24(10):3098. doi: 10.3390/s24103098.
The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.
边缘计算系统与现场可编程门阵列（FPGA）技术的融合在增强各领域实时应用方面展现出了巨大潜力。本文提出了一种专门为高级驾驶辅助系统（ADAS）领域的路面缺陷检测量身定制的创新型边缘计算系统设计。该系统将AMD赛灵思人工智能平台无缝集成到定制电路配置中，充分利用其功能。系统利用摄像头作为输入传感器来捕捉道路场景，采用深度学习处理单元（DPU）执行YOLOv3模型，能够高精度、高效率地识别三种不同类型的路面缺陷。在缺陷检测之后，系统通过控制器局域网（CAN）接口高效传输有关检测到的缺陷类型和位置的详细信息。这种基于FPGA的边缘计算集成不仅提高了缺陷检测的速度和准确性，还促进了车辆车载控制器与外部系统之间的实时通信。此外，所提出系统的成功集成将ADAS转变为一个复杂的边缘计算设备，使车辆车载控制器能够实时做出明智决策。这些决策旨在通过改善安全和性能指标来提升整体驾驶体验。边缘计算与FPGA技术之间的协同作用不仅提升了ADAS的能力，还为汽车安全和辅助系统的未来创新铺平了道路。
