烟雾检测技术在预防早期火灾蔓延方面具有重要的作用,准确快速的烟雾检测算法具有非常重要的实际应用价值。近年来,随着机器视觉与图像处理技术的快速发展,面向视频图像的烟雾检测算法以其非接触性、强鲁棒性等特点受到广泛的关注。面向视频图像的烟雾检测算法有效克服了传统烟雾探测器靠近火源才能工作的不足,但是由于场景的复杂性和环境因素的不确定性,面向视频图像的烟雾检测算法仍然面临着巨大的挑战。首先简单介绍了烟雾检测技术的基本流程,包括预处理、特征提取以及分类识别等步骤;其次介绍了基于颜色和运动分割的预处理方法,进一步分析了烟雾的视觉特性和运动特性,并介绍了相关烟雾特征提取算法;然后,对当前烟雾检测常用的分类器以及深度学习网络模型进行了探讨和总结;最后,重点介绍了烟雾检测算法存在的不足,并对烟雾检测算法的发展进行了展望。

The smoke detection technology plays an important role in preventing early fire spread. An accurate and fast smoke detection algorithm has very important practical application value. In recent years, with the rapid development of machine vision and image processing technology, smoke detection algorithms for video-oriented images have attracted extensive attention due to their non-contact and strong robustness. The smoke detection algorithm based on video images can effectively overcome the deficiency of traditional smoke detectors working close to fire sources. However, the smoke detection algorithm based on video images still faces great challenges due to the complexity of scenes and the uncertainty of environmental factors. First, the basic process of the smoke detection technology is briefly introduced, including pretreatment, feature extraction, and classification recognition. Second, the preprocessing method based on color and motion segmentation is introduced, and the visual characteristics and movement characteristics of smoke are further analyzed and the related smoke extraction algorithms are introduced. Third, some of the current commonly used smoke detection classifiers and the deep learning network models are discussed and summarized. Finally, the deficiencies of the smoke detection algorithm are mainly introduced and the future of the smoke detection algorithm is prospected.