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.

翻译：“ 你只看一次 v4” (YOLOv4) 是一种深层次学习中的物体探测方法。 根据 YOLOv4 推荐 YOLOv4- tiny 来简化网络结构并减少参数, 从而适合移动和嵌入设备开发。 为了改进物体探测的实时, 根据 YOLOv4- tiny 提出了快速物体探测方法。 它首先在 ResNet- D 网络中使用两个 ResBlock- D 模块, 而不是在 Yolov4- tiny 中的两个 CSPBlock 模块。 第二, 它设计了一个辅助剩余网络块来提取更多物体特征信息以减少探测错误。 在辅助网络的设计中, 使用两个连续的 3x3x3 变换来获取 5x5 接收场以提取全球特性, 并使用 传送注意力和空间关注来获取更有效的信息。 在最后, 它将辅助网络和主干网合并来构建整个网络结构, 来降低计算复杂性。 第二, 它设计了一个辅助网络块来提取更多的物体特性信息, 以更快地探测 YOL4 。 和 平均的精确度为 YOL4 。