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首页> 外文会议>International Siberian Conference on Control and Communications >Convolutional neural networks of the YOLO class in computer vision systems for mobile robotic complexes
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Convolutional neural networks of the YOLO class in computer vision systems for mobile robotic complexes

机译：移动机器人复合体计算机视觉系统中YOLO类的卷积神经网络

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An important scientific direction is the development and study of computer vision systems (CVS) for mobile robotic complexes. Today, developers of CVS are most often using convolutional neural networks (CNN). For increasing the speed detection of objects on images in CVS, there has been a trend of using CNN, which are hardware-implemented on field-programmable gate array (FPGAs).This article shows that the perspective for hardware implementation on the FPGA is the tiny-YOLO CNN from the YOLO class. For reduce required FPGA computing resources in this CNN, was proposed to use Inception-ResNet modules. We was found that with high detection accuracy of objects in images with minimum resources requirements provide by the tiny-YOLO-Inception-ResNet2 network architecture. It is obtained from replacing the fifth tiny-YOLO convolutional layer of the tiny-YOLO CNN with two sequential processing Inception-ResNet modules. Also results of the study of the detection accuracy of objects using the CNN for this architecture with the lack of resource-intensive operations: batch normalization and bias from calculations were given. These studies were performed for different formats of representation numbers in the FPGA.
机译：一个重要的科学方向是用于移动机器人复合体的计算机视觉系统（CVS）的开发和研究。如今，CVS的开发人员最常使用卷积神经网络（CNN）。为了提高CVS中图像上对象的检测速度，有一种使用CNN的趋势，CNN是在现场可编程门阵列（FPGA）上硬件实现的。来自YOLO类的tiny-YOLO CNN。为了减少此CNN中所需的FPGA计算资源，建议使用Inception-ResNet模块。我们发现，微小的YOLO-Inception-ResNet2网络体系结构提供了具有最低资源要求的图像中对象的高检测精度。它是通过用两个顺序处理的Inception-ResNet模块替换tiny-YOLO CNN的第五个tiny-YOLO卷积层获得的。在缺乏资源密集型操作的情况下，针对这种架构使用CNN进行对象检测精度研究的结果：还给出了批处理归一化和计算偏差。这些研究是针对FPGA中不同形式的表示编号进行的。

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