针对火焰检测算法在复杂场景下误检率高、算法适应性差、效率低等问题，设计一种轻量高效的两阶段视频火焰检测算法。第一阶段采用改进的自适应高斯混合模型（adaptive gaussian mixture model, AGMM）对视频图像序列进行快速背景建模，利用火焰的闪烁和涌动特性，提取出序列中的可疑候选区域。第二阶段使用残差深度归一化卷积神经网络 （residual deep normalization and convolutional neural network, ResDN）对可疑候选区域进行判别，并引入简化的残差块替换原有的卷积层进行轻量化设计，实现对火焰的检测与定位。相比于传统分类算法，所设计的两阶段视频火焰检测算法能够有效克服复杂场景下的环境干扰，准确快速地识别火焰，具有更高的检测率和适应性。

A lightweight and efficient two-stage video flame detection algorithm was designed to address issues of high false positive rates, poor adaptability, and low efficiency in complex scenes. In the first stage, an improved adaptive Gaussian mixture model(AGMM) was employed for rapid background modeling of video image sequences. Suspicious candidate regions were extracted from the sequences by leveraging the flickering and surging characteristics of flames. In the second stage, a residual deep normalization and convolutional neural network (ResDN) was used to discriminate these suspicious candidate regions. A simplified residual block was introduced to replace the original convolutional layers for a lightweight design, enabling accurate flame detection and localization. Compared with traditional classification algorithms, the proposed two-stage video flame detection algorithm effectively overcomes environmental interference in complex scenes, rapidly and accurately identifies flames, and demonstrates higher detection rates and adaptability.