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.

In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.