Abstract
Smoke is the first visible indicator of a this http URL the advancement of deep learning, image-based smoke detection has become a crucial method for detecting and preventing forest fires. However, the scarcity of smoke image data from forest fires is one of the significant factors hindering the detection of forest fire smoke. Image generation models offer a promising solution for synthesizing realistic smoke images. However, current inpainting models exhibit limitations in generating high-quality smoke representations, particularly manifesting as inconsistencies between synthesized smoke and background contexts. To solve these problems, we proposed a comprehensive framework for generating forest fire smoke images. Firstly, we employed the pre-trained segmentation model and the multimodal model to obtain smoke masks and image this http URL, to address the insufficient utilization of masks and masked images by inpainting models, we introduced a network architecture guided by mask and masked image features. We also proposed a new loss function, the mask random difference loss, which enhances the consistency of the generated effects around the mask by randomly expanding and eroding the mask this http URL, to generate a smoke image dataset using random masks for subsequent detection tasks, we incorporated smoke characteristics and use a multimodal large language model as a filtering tool to select diverse and reasonable smoke images, thereby improving the quality of the synthetic dataset. Experiments showed that our generated smoke images are realistic and diverse, and effectively enhance the performance of forest fire smoke detection models. Code is available at this https URL.
Abstract (translated)
烟是森林火灾早期最明显的视觉标志之一。随着深度学习的进展，基于图像的烟雾检测已成为发现和预防森林火灾的关键方法。然而，缺乏来自实际森林火灾的烟雾图像数据是阻碍有效检测的一个重要因素。图像生成模型为合成逼真的烟雾图像提供了一个有前景的解决方案。但是，现有的修复模型在生成高质量的烟雾表示时存在局限性，尤其表现为合成出的烟雾与其背景环境之间的一致性不足。 为了解决这些问题，我们提出了一种用于生成森林火灾烟雾图像的全面框架。首先，我们利用预训练的分割模型和多模态模型获取烟雾掩码（即标记了烟雾区域的图像）。为了克服修复模型对这些烟雾掩码及掩码后图像利用不足的问题，我们引入了一个由掩码与掩码后图像特征引导的网络架构。此外，我们提出了一种新的损失函数——随机差异掩码损失，通过随机扩展和腐蚀掩码来增强生成效果在掩码周围的一致性。 为了使用随机掩码生成烟雾图像数据集，以供后续检测任务使用，并提高合成数据集的质量，我们结合了烟雾特征并采用多模态大型语言模型作为过滤工具，选择多样且合理的烟雾图像。 实验结果表明，我们生成的烟雾图像既逼真又多样化，能有效提升森林火灾烟雾检测模型的性能。代码可在[提供的URL]获取。
URL
https://arxiv.org/abs/2507.11252
