基于异构多处理器平台的草莓检测
Strawberry Detection Using a Heterogeneous Multi-Processor Platform
摘要：
在过去的几年里，精准农业项目的数量有所增加，特别是在收获机器人方面，其中许多项目从识别作物到抓取所需的水果或蔬菜都取得了持续的进展。在精准农业项目中发现的最常见的问题之一是，成功的应用不仅严重依赖于识别水果，还依赖于确保定位允许准确的导航。当机器人不在预先安排的环境中操作时，或者当植被变得太厚从而覆盖作物时，这些问题成为重要因素。此外，在嵌入式平台上运行最先进的深度学习算法也非常具有挑战性，导致大多数时候帧速率较低。本文提出了使用你只看一次版本3（YOLOv3）卷积神经网络（CNN）结合利用图像处理技术的应用，精准农业机器人针对草莓检测，加速异构多处理器平台。结果表明，当在现场可编程门阵列（FPGA）上实现时，与在处理器侧上运行的相同算法相比，在由146幅图像组成的测试集上的准确度为78.3%时，性能加速了5倍。
英文摘要：
Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
