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- W4360996572 abstract "The trade-off between better accuracy and efficiency is an important problem in smoke detection. In deep networks, low-level features are generally considered to be poor in semantic information but rich in keeping geometric details, which is the opposite for high-level features. Therefore, feature fusion plays a crucial role in combining semantic and geometric information. The transformer encoder has the advantages of global computations and perfect memory that searches each element of a sequence and updates it by aggregating information from the entire sequence. In this paper, we proposed a block that combines the geometric information of the low-level features and the semantic information of the high-level features of the backbone with the transform encoder. We boost the fuse-transformer YOLOX-Nano to 44.28% AP on VOC0712 training datasets and on VOC07 test datasets with 416 x 416 resolution, outperforming the original YOLOX-Nano 43.32% AP. If converted to a smoke datasets which containing 9726 training images and 2320 test images. We increase the fuse transformer YOLOX-Nano to 31.48% AP on smoke datasets with a resolution of 416 × 416, outperforming the original YOLOX-Nano by 30.9% AP." @default.
- W4360996572 created "2023-03-30" @default.
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- W4360996572 date "2022-11-11" @default.
- W4360996572 modified "2023-10-17" @default.
- W4360996572 title "Fusing Transformer and YOLOX for Smoke Detection" @default.
- W4360996572 doi "https://doi.org/10.1109/icct56141.2022.10073048" @default.
- W4360996572 hasPublicationYear "2022" @default.
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