Spagnolo Fanny, Perri Stefania, Corsonello Pasquale
Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, Italy.
Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy.
Sensors (Basel). 2019 Jul 11;19(14):3055. doi: 10.3390/s19143055.
Connected Component Analysis (CCA) plays an important role in several image analysis and pattern recognition algorithms. Being one of the most time-consuming tasks in such applications, specific hardware accelerator for the CCA are highly desirable. As its main characteristic, the design of such an accelerator must be able to complete a run-time process of the input image frame without suspending the input streaming data-flow, by using a reasonable amount of hardware resources. This paper presents a new approach that allows virtually any feature of interest to be extracted in a single-pass from the input image frames. The proposed method has been validated by a proper system hardware implemented in a complete heterogeneous design, within a Xilinx Zynq-7000 Field Programmable Gate Array (FPGA) System on Chip (SoC) device. For processing 640 × 480 input image resolution, only 760 LUTs and 787 FFs were required. Moreover, a frame-rate of ~325 fps and a throughput of 95.37 Mp/s were achieved. When compared to several recent competitors, the proposed design exhibits the most favorable performance-resources trade-off.
连通分量分析（CCA）在多个图像分析和模式识别算法中发挥着重要作用。作为此类应用中最耗时的任务之一，非常需要用于CCA的特定硬件加速器。作为其主要特性，这种加速器的设计必须能够通过使用合理数量的硬件资源，在不中断输入流数据的情况下完成输入图像帧的运行时处理。本文提出了一种新方法，该方法允许在单次遍历中从输入图像帧中提取几乎任何感兴趣的特征。所提出的方法已通过在完整的异构设计中实现的适当系统硬件进行了验证，该硬件位于赛灵思Zynq-7000片上现场可编程门阵列（FPGA）系统级芯片（SoC）设备中。对于处理640×480的输入图像分辨率，仅需要760个查找表（LUT）和787个触发器（FF）。此外，实现了约325帧/秒的帧率和95.37兆像素/秒的吞吐量。与最近的几个竞争对手相比，所提出的设计展现出了最有利的性能-资源权衡。
