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Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection

Author

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  • Yaxin Wang

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Xinyuan Liu

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Fanzhen Wang

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Dongyue Ren

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Yang Li

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Zhimin Mu

    (College of Basic Science, Tianjin Agricultural University, Tianjin 300392, China)

  • Shide Li

    (College of Basic Science, Tianjin Agricultural University, Tianjin 300392, China)

  • Yongcheng Jiang

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

Abstract

Fuel types are essential for the control systems of briquette biofuel boilers, as the optimal combustion condition varies with fuel type. Moreover, the use of coal in biomass boilers is illegal in China, and the detection of coals will, in time, provide effective information for environmental supervision. This study established a briquette biofuel identification method based on the object detection of fuel images, including straw pellets, straw blocks, wood pellets, wood blocks, and coal. The YoloX-S model was used as the baseline network, and the proposed model in this study improved the detection performance by adding the self-attention mechanism module. The improved YoloX-S model showed better accuracy than the Yolo-L, YoloX-S, Yolov5, Yolov7, and Yolov8 models. The experimental results regarding fuel identification show that the improved model can effectively distinguish biomass fuel from coal and overcome false and missed detections found in the recognition of straw pellets and wood pellets by the original YoloX model. However, the interference of the complex background can greatly reduce the confidence of the object detection method using the improved YoloX-S model.

Suggested Citation

  • Yaxin Wang & Xinyuan Liu & Fanzhen Wang & Dongyue Ren & Yang Li & Zhimin Mu & Shide Li & Yongcheng Jiang, 2023. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14437-:d:1252811
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    References listed on IDEAS

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