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- W4213330714 abstract "Because of small targets and small samples size of Myrica rubra, the application of intelligent technologies such as deep learning in Myrica rubra target detection is restricted. Based on the improved Yolo-v5 model, and the channel attention mechanism is used by SE module embedded in backbone. And the maximum sampling times are reduced by the limit of PANet layers. By these ways, the repeated superposition of background features is suppressed, and the ability of impoved model to extract small target feature is enhanced. The model of Myrica rubra detection is fully trained by transfer learning and small samples size. The experiment result proves that improved model is suitable to target detection of Myrica rubra." @default.
- W4213330714 created "2022-02-24" @default.
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- W4213330714 date "2021-12-01" @default.
- W4213330714 modified "2023-09-26" @default.
- W4213330714 title "Target Detection of Myrica rubra Based on Convolutional Neural Network and Transfer Learning" @default.
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- W4213330714 doi "https://doi.org/10.1109/iceitsa54226.2021.00098" @default.
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