Fire Detection and Recognition Based on MATLAB

Fire detection technology based on MATLAB typically combines image processing and pattern recognition methods to achieve accurate identification of flame regions. The core workflow can be divided into the following steps: Image Preprocessing Color space conversion (such as RGB to HSV or YCrCb) is implemented to enhance chrominance characteristics of flames. Gaussian filtering or median filtering techniques are applied using functions like imgaussfilt() or medfilt2() to reduce noise interference, thereby improving the accuracy of subsequent analysis. Flame Feature Extraction Typical flame characteristics include dynamic texture (irregular edges), color distribution (red/yellow highlight regions), and flickering frequency. MATLAB can capture dynamic changes through frame difference methods using diff() or vision.ForegroundDetector, combined with threshold segmentation techniques (e.g., based on Cr channel thresholds via imbinarize()) to isolate candidate regions. Classification and Validation Traditional methods rely on rule-based judgments (such as area and circularity calculated using regionprops()), while modern approaches often employ machine learning algorithms (SVM with fitcsvm() or CNN with trainNetwork()) to train classifiers for distinguishing between flame and non-flame regions. MATLAB's Computer Vision Toolbox and Deep Learning Toolbox provide convenient interfaces for these implementations. Optimization and Deployment Parameter adjustment (such as dynamic threshold sensitivity) helps reduce false detections. The final output can include flame position markers created with insertShape() or warning signals, making it suitable for security surveillance or fire prevention systems. Extension Ideas: Integration of infrared image data or multi-sensor information can enhance robustness in complex environments through data fusion techniques like imfuse() or sensor data synchronization.