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The "You only look once v4"(YOLOv4) is one type of object detection methodsin deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the networkstructure and reduce parameters, which makes it be suitable for developing onthe mobile and embedded devices. To improve the real-time of object detection,a fast object detection method is proposed based on YOLOv4-tiny. It firstlyuses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modulesin Yolov4-tiny, which reduces the computation complexity. Secondly, it designsan auxiliary residual network block to extract more feature information ofobject to reduce detection error. In the design of auxiliary network, twoconsecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extractglobal features, and channel attention and spatial attention are also used toextract more effective information. In the end, it merges the auxiliary networkand backbone network to construct the whole network structure of improvedYOLOv4-tiny. Simulation results show that the proposed method has faster objectdetection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value ofaverage precision as the YOLOv4-tiny. It is more suitable for real-time objectdetection.

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