Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

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چکیده

For the detection of small targets, fire-like and smoke-like targets in forest fire images, as well under different natural lights, an improved Fire-YOLO deep learning algorithm is proposed. The model expands feature extraction network from three dimensions, which enhances propagation identification, improves performance, reduces parameters. Furthermore, through promotion pyramid, top-performing prediction box obtained. attains excellent results compared to state-of-the-art object networks, notably smoke. Overall, can effectively deal with inspection objects. When input image size 416 × resolution, average time 0.04 s per frame, provide real-time detection. Moreover, proposed this paper also be applied target other complicated situations.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2022

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su14094930