ArticleScientific reports2025
Enhanced YOLOv7 with CDP-ELAN and gather-distribute mechanism for robust smoke and flame detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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