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<p>Fire detection is crucial for safeguarding human life and property. To address the limitations of existing deep learning-based detectors—such as weak feature perception, information loss, high computational cost, and poor performance on small targets—this paper proposes an enhanced YOLOv7 model named CGDS-YOLO. The model introduces three key innovations: a CDP-ELAN module (fusing Coordinate Convolution, Diverse Branch Block, and Partial Convolution) for strengthened feature extraction, a Gathering-Distributing mechanism for improved multi-scale information fusion, and a SlimNeck structure to reduce parameters while retaining fine-grained details. Additionally, Normalized Wasserstein Distance is adopted to enhance small target detection. Experiments on a homemade smoke and flame dataset and the public Visdrone dataset show that CGDS-YOLO outperforms baseline models, improving mAP by 2.0% and 1.7%, respectively, while maintaining high computational efficiency.</p>

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Enhanced YOLOv7 with CDP-ELAN and gather-distribute mechanism for robust smoke and flame detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Junjie Hu,
  • Siyu He,
  • Yingjing Qian,
  • Qingli Zeng,
  • Yibo Niu,
  • Renmin Zhang

摘要

Fire detection is crucial for safeguarding human life and property. To address the limitations of existing deep learning-based detectors—such as weak feature perception, information loss, high computational cost, and poor performance on small targets—this paper proposes an enhanced YOLOv7 model named CGDS-YOLO. The model introduces three key innovations: a CDP-ELAN module (fusing Coordinate Convolution, Diverse Branch Block, and Partial Convolution) for strengthened feature extraction, a Gathering-Distributing mechanism for improved multi-scale information fusion, and a SlimNeck structure to reduce parameters while retaining fine-grained details. Additionally, Normalized Wasserstein Distance is adopted to enhance small target detection. Experiments on a homemade smoke and flame dataset and the public Visdrone dataset show that CGDS-YOLO outperforms baseline models, improving mAP by 2.0% and 1.7%, respectively, while maintaining high computational efficiency.