Real-time object detection method based on improved YOLOv4-tiny

Real-time object detection method based on improved YOLOv4-tiny

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.

基于改进的YOLOv4-tiny的实时目标检测方法

“您只看一次v4”（YOLOv4）是深度学习中的一种对象检测方法。YOLOv4-tiny是基于YOLOv4提出的，它可以简化网络结构并减少参数，使其适合在移动和嵌入式设备上进行开发。.. 为了提高目标检测的实时性，提出了一种基于YOLOv4-tiny的快速目标检测方法。它首先使用ResNet-D网络中的两个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度。其次，设计了辅助残差网络块，以提取更多的物体特征信息，以减少检测误差。在辅助网络的设计中，使用两个连续的3x3卷积获得5x5接收场以提取全局特征，并使用信道注意力和空间注意力来提取更有效的信息。最后，将辅助网络和骨干网络合并，以构建改进的YOLOv4-tiny的整个网络结构。仿真结果表明，该方法具有比YOLOv4-tiny和YOLOv3-tiny更快的目标检测速度，并且其平均精度的平均值与YOLOv4-tiny几乎相同。它更适合于实时物体检测。 （阅读更多）