论文标题
实时对象检测方法基于改进的Yolov4微小
Real-time object detection method based on improved YOLOv4-tiny
论文作者
论文摘要
“您只看一次V4”（Yolov4）是深度学习中一种类型的对象检测方法。 Yolov4-tiny是基于Yolov4提出的，以简化网络结构并减少参数，这使其适合在移动设备和嵌入式设备上开发。为了改善对象检测的实时，提出了一种基于yolov4微小的快速对象检测方法。它首先在Resnet-D网络中使用两个Resblock-D模块，而不是Yolov4-tiny中的两个CSPBLOCK模块，从而降低了计算复杂性。其次，它设计了一个辅助残差网络块，以提取对象的更多特征信息以减少检测错误。在辅助网络的设计中，使用两个连续的3x3卷积来获得5x5接收场来提取全局特征，并且还使用了通道的注意力和空间注意力来提取更有效的信息。最后，它合并了辅助网络和骨干网络，以构建改进的Yolov4微型的整个网络结构。仿真结果表明，所提出的方法比Yolov4-tiny和Yolov3小型具有更快的对象检测，而平均精度的平均值几乎与Yolov4-tiny相同。它更适合实时对象检测。
The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.
