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Article overview
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Real-time object detection method based on improved YOLOv4-tiny | | Zicong Jiang
; Liquan Zhao
; Shuaiyang Li
; Yanfei Jia
; | | Date: |
9 Nov 2020 | | Abstract: | 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. | | Source: | arXiv, 2011.04244 | | Services: | Forum | Review | PDF | Favorites | |
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