Abstract
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.
Abstract (translated)
嵌入式视觉系统需要高效且鲁棒的图像处理算法，以在资源受限的硬件上实现实时性能。本研究调查了在包括DSP和FPGA在内的嵌入式处理器上实现的图像处理算法，特别是边缘检测、角点检测和Blob（连通区域）检测算法。为解决图像处理文献中提到的延迟、准确性和功耗问题，采用了优化的算法架构和量化技术。此外，还使用了最佳的技术来消除帧间冗余并进行自适应帧平均，以在保证合理图像质量的前提下提高吞吐量。 对所提出方法的仿真和硬件试验表明，与传统实现相比，在处理速度和能效方面有了显著改进。本研究的进步为汽车、监控和机器人领域的可扩展且低成本的嵌入式成像系统铺平了道路，并强调了针对实际实时嵌入式视觉应用联合设计算法和硬件架构的好处。
URL
https://arxiv.org/abs/2601.06243
