为了解决矿井复杂环境下外因火灾监测误报率和漏报率较高的问题，提出基于红外视觉特征融合的矿井外因火灾监测算法。首先，改进红外小目标检测的局部对比度度量(LCM)模型，提高早期火灾目标的显著度，进而分割出火灾疑似区域；其次，通过分析不同监视场景下外因火灾和主要干扰热源在热红外图像序列中的视觉特征，选出抗干扰能力强的火灾显著特征；然后，优选火灾显著特征提取方法和相似度估计策略，以获取热红外图像序列中火灾疑似区域的主要视觉特征，并构建火灾特征向量；最后，通过建立特征向量集，构建基于支持向量机(SVM)的矿井外因火灾检测模型，对所提算法进行验证。结果表明：所提算法不仅能监测不同场景下的外因火灾，还能够监测远距离和早期阶段的外因火灾，其正确率和检测率分别达到96.93 %、96.24 %，误检率低至2.56 %；相较于对比算法，所提算法在火灾监测的准确率、误报率和漏报率方面均有较大的改善。

In order to solve the problems of high false positive and false negative rates of external fire monitoring in complex mine environments, a monitoring algorithm using infrared visual feature fusion was proposed. Firstly, the Local Contrast Measure (LCM) model for infrared small target detection was improved to enhance the saliency of early-stage fire targets, thereby segmenting out suspected fire areas. Then, by analyzing the visual features of exogenous fires and major interfering heat sources in thermal infrared image sequences under different surveillance scenarios, the salient features of fires with strong anti-interference ability were preferred. Next, fire salient feature extraction methods and similarity estimation strategies were optimized to obtain the main visual features of suspected fire areas in the thermal infrared image sequences and construct a fire feature vector. Finally, by establishing a feature vector set and constructing a mine exogenous fire detection model using Support Vector Machine (SVM), the proposed algorithm was experimentally validated. The results show that the proposed algorithm realizes exogenous fire monitoring in different scenarios, as well as in remote and early stages, with accuracy and detection rates of 96.93 % and 96.24 %, respectively, and a false detection rate of 2.56 %. Compared to the described comparison algorithms, the proposed method has better improvements in the accuracy, false alarm rate, and leakage alarm rate of fire monitoring.