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首页> 外文会议>International Conference on Computing Methodologies and Communication >FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA
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FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA

机译：用于对象检测和识别的基于FPGA的深度学习模型对象检测比较使用FPGA的对象检测模型比较

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Real-time object detection and recognition finds extensive applications in diverse fields such as medical applications, security surveillance, and autonomous vehicles. There are many machines and deep learning techniques that are employed for object detection and recognition. The emergence of a convolutional neural network (CNN) has provided a major breakthrough for object detection and recognition. This work also includes the hardware implementation of the same with the help of Xilinx PYNQ Z2 and Intel Movidius Neural Compute Stick (NCS) which are proved to increase the performance of the system proposed. The results are compared based on three deep learning methods: Single Shot Detector (SSD), Faster Region CNN (FRCNN), You Only Look Once (YOLO). The parameters that are considered are frames per second, probability of detection, and time for computation. The results obtained are performing well compared to existing models.
机译：实时对象检测和识别在医疗应用，安全监控和自动驾驶汽车等不同领域中找到了广泛的应用。有许多机器和深度学习技术可用于对象检测和识别。卷积神经网络（CNN）的出现为目标检测和识别提供了重大突破。这项工作还包括在Xilinx PYNQ Z2和Intel Movidius神经计算棒（NCS）的帮助下实现了相同的硬件，这被证明可以提高所提出的系统的性能。根据三种深度学习方法对结果进行比较：单发检测器（SSD），快速区域CNN（FRCNN），只看一次（YOLO）。所考虑的参数是每秒帧数，检测概率和计算时间。与现有模型相比，获得的结果表现良好。

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