An FPGA based Real-Time Flat Field Correction for Infrared Focal Plane Arrays

Njuguna, Josphat Chege; Alabay, Emre; Çelebi, Anıl; Çelebi, Aysun Taşyapı; Güllü, Mehmet Kemal

doi:10.1109/asyu58738.2023.10296698

Cited by 1 publication

(1 citation statement)

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“…This approach effectively improves efficiency in image transmission within the FPGA system. Another effective solution for image system is the utilization of Video Direct Memory Access (VDMA) [19][20][21]. Its primary function involves implementing DMA technology to efficiently move video data in video processing applications.…”

Section: Customer Circuit Designmentioning

confidence: 99%

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

Chi,

Chen,

Lin

et al. 2024

Sensors

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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.

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Section: Customer Circuit Designmentioning

confidence: 99%