Multi-target detection method based on YOLOv4 convolutional neural network

doi:10.1088/1742-6596/1883/1/012075

Multi-target detection method based on YOLOv4 convolutional neural network

2021-04-01

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DOI：10.1088/1742-6596/1883/1/012075

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Abstract In order to solve the problems of low detection accuracy, false detection and high miss detection rate of small targets in target detection tasks, this paper is a multi-target detection method based on YOLOv4 convolutional neural network. The proposed method is based on YOLOv4. The semantic information of high-level features is first propagated to the low-level network through FPN sampling, and then it is fused with the high-resolution information of the underlying features to improve the detection effect of small target detection objects. The information transmission path from the bottom to the top is enhanced by downsampling the feature pyramid, and finally the feature maps of different layers are fused to achieve relevant predictions. Experiments prove that the method proposed in this paper has good results.

Multi-target detection method based on YOLOv4 convolutional neural network

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