Abstract: 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.