A Novel Bird Detection and Identification based on DPU processor on PYNQ FPGA
A Novel Bird Detection and Identification based on DPU processor on PYNQ FPGA
复制标题
一种基于PYNQ FPGA上的DPU处理器的新型鸟类检测与识别
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Sheu
中科院分区：
文献类型：
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作者：
Guan;M. H. Nguyen;Chi;Po;M. Sheu
在本文中，在Pynq FPGA上提出了深度学习鸟类的识别，并具有SOC架构。新的检测方法可以分为移动对象检测和神经网络处理器体系结构。移动对象检测基于框架差的原理以获得图像标签。通过形态，模糊和二进制处理后，记录的帧导致其大小和位置在图像中检测到的移动对象。确认的移动物体通过深度学习的处理器（DPU）推动进行分类，从而导致鸟类的类型。实验的结果表明，提出的方法可以使用126.8 GOP/S/W功率效率达到84.3％的精度，这非常适合在森林或室外环境中进行的低功率监视实验。
In this paper, deep learning bird identification is proposed and implemented on PYNQ FPGA with SoC architecture. The new detection method can be divided into moving object detection, and neural network processor architecture. The moving object detection is based on the principle of frame difference to obtain the image label. The recorded frames after being processed through morphology, fuzzy and binarization result in the moving object detected with its size and position within the image. The confirmed moving object is pushed through a deep-learning processor unit (DPU) for classification, resulting in the type of the bird. The results of the experiment show that the proposed method can reach 84.3% accuracy with 126.8 GOP/s/W power efficiency, which is very suitable for low power surveillance experiments in forests or outdoor environments.