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- W4367308021 abstract "Aiming at the problems of low detection accuracy, poor real-time and robustness in vehicle target detection in the field of autonomous driving and other fields of existing target detection algorithms, this paper proposes a model based on YOLOv5s and introduces an attention mechanism to fuse multi-scale features. The detection algorithm of YOLOv5s model adds a layer of CBAM(Convolutional Block Attention Module) before the SPPF of Backbone of the YOLOv5s model, and uses ACON(Acrivate or Not) to replace ReLU as the activation function of the network, which improves the detection accuracy and can be effectively applied to vehicle detection in complex scenes." @default.
- W4367308021 created "2023-04-29" @default.
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- W4367308021 date "2023-04-28" @default.
- W4367308021 modified "2023-10-16" @default.
- W4367308021 title "Improved vehicle detection based on YOLOv5s" @default.
- W4367308021 doi "https://doi.org/10.1117/12.2674439" @default.
- W4367308021 hasPublicationYear "2023" @default.
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