为嵌入式设备优化图像处理算法，提升实时性与能效。
Real-Time Image Processing Algorithms for Embedded Systems
- 针对边缘、角点和斑块检测设计轻量算法架构
- 实测速度与能效显著优于传统方案
- 适合汽车、安防、机器人等实时视觉场景
嵌入式视觉系统需在资源受限的硬件上实现高效可靠的实时图像处理。本研究聚焦边缘检测、角点检测和斑块检测算法，在DSP和FPGA等嵌入式处理器上实现。为解决文献中普遍存在的延迟、精度与功耗问题，采用优化算法架构与量化技术；同时结合帧间冗余消除与自适应帧平均技术，提升吞吐量并保持合理图像质量。仿真与硬件实验表明，所提方法在处理速度与能效方面较传统实现有显著提升。该研究推动了可扩展、低成本嵌入式成像系统在汽车、监控与机器人领域的应用，强调了算法与硬件协同设计对实际嵌入式视觉系统的重要性。
原文摘要 · Abstract (English)
Embedded vision systems need efficient and robust image processing algorithms to perform real-time, with resource-constrained hardware. This research investigates image processing algorithms, specifically edge detection, corner detection, and blob detection, that are implemented on embedded processors, including DSPs and FPGAs. To address latency, accuracy and power consumption noted in the image processing literature, optimized algorithm architectures and quantization techniques are employed. In addition, optimal techniques for inter-frame redundancy removal and adaptive frame averaging are used to improve throughput with reasonable image quality. Simulations and hardware trials of the proposed approaches show marked improvements in the speed and energy efficiency of processing as compared to conventional implementations. The advances of this research facilitate a path for scalable and inexpensive embedded imaging systems for the automotive, surveillance, and robotics sectors, and underscore the benefit of co-designing algorithms and hardware architectures for practical real-time embedded vision applications.
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