Drones 2025, 9(3), 217; https://doi.org/10.3390/drones9030217 - 18 Mar 2025
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Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we first constructed a dataset
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Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we first constructed a dataset of infrared and visible light images captured in the same scene, from the same perspective, and at the same time, with pixel-level segmentation annotations of the flame regions in the images. In response to the issues of poor flame segmentation performance in the current fire images and the large number of learnable parameters in large models, we propose an improved large model algorithm, SAMFA (Segmentation Anything Model, Fire, Adapter). Firstly, while freezing the original parameters of the large model, only the additionally incorporated Adapter module is fine-tuned to better adapt the network to the specificities of the flame segmentation task. Secondly, to enhance the network’s perception of flame edges, a U-shaped mask decoder is designed. Lastly, to reduce the training difficulty, a progressive strategy combining self-supervised and fully supervised learning is employed to optimize the entire model. We compared SAMFA with five state-of-the-art image segmentation algorithms on a labeled public dataset, and the experimental results demonstrate that SAMFA performs the best. Compared to SAM, SAMFA improves the IoU by 11.94% and 6.45% on infrared and visible light images, respectively, while reducing the number of learnable parameters to 11.58 M.
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