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- W4386821124 abstract "To address the real-time detection of smoke and fire in a variety of scenarios, a lightweight model M-YOLOX based on the YOLOX-S is proposed in this paper, of which the improvements include replacing the backbone network of the YOLOX-S model with the backbone network of the MobileNetV3 model and appending a SPPBottleneck layer, adding the Convolutional Block Attention Module (CBAM) after the CspLayer layer, optimizing the loss function. The proposed M-YOLOX model and the current mainstream models are all trained using self-built image datasets, and the detection results are compared. The experimental results show that the proposed M-YOLOX model has a comprehensive advantage in terms of detection accuracy, number of model parameters and detection speed, and is more suitable for embedded systems with limited computational resources." @default.
- W4386821124 created "2023-09-19" @default.
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- W4386821124 date "2023-07-24" @default.
- W4386821124 modified "2023-09-26" @default.
- W4386821124 title "Smoke/Fire Detection with an Improved YOLOX Model" @default.
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- W4386821124 doi "https://doi.org/10.23919/ccc58697.2023.10241194" @default.
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