MATLAB Code Implementation for Flame Detection

Flame detection is an important application in computer vision, commonly used in fire early warning systems. Implementing flame detection in MATLAB typically involves the following key steps:

### 1. Color Space Conversion Flames exhibit high red and yellow components in the RGB color space, so color threshold segmentation can be used to initially extract flame regions. A common approach involves converting images from RGB to HSV or YCbCr space to enhance the differentiation of flame colors. Implementation tip: Use MATLAB's rgb2hsv() or rgb2ycbcr() functions for efficient color space conversion.

### 2. Color Threshold Segmentation In HSV or YCbCr space, flame colors typically show high saturation (S) and high brightness (V), or specific Cb and Cr component ranges. By setting appropriate thresholds, pixels matching flame color characteristics can be identified. Code example: Utilize logical indexing and relational operators to create binary masks based on threshold values for each color channel.

### 3. Dynamic Feature Analysis Flames exhibit dynamic characteristics such as flickering and movement. Motion patterns can be detected using frame differencing or optical flow methods between consecutive frames to eliminate static red interference objects (like red clothing or signs). Algorithm note: Implement frame subtraction using imabsdiff() function or use vision.OpticalFlow for motion analysis.

### 4. Morphological Processing The segmented flame regions may contain noise or discontinuous parts. Morphological operations (such as dilation and erosion) can smooth region boundaries and merge adjacent flame pixel clusters. Implementation approach: Apply imdilate() and imerode() functions with appropriate structuring elements to refine the detected regions.

### 5. Region Filtering Further filter candidate regions based on flame geometric characteristics (such as area, aspect ratio) to exclude small areas or shapes that don't match flame properties, improving detection accuracy. Technical detail: Use regionprops() function to calculate area, bounding box dimensions, and other geometric properties for region validation.

### 6. Result Visualization Finally, mark the detected flame regions in the original image (using rectangular bounding boxes or contour highlighting) for observation and verification. Code implementation: Employ insertShape() or bwboundaries() functions to overlay detection results on the original frame.

The advantage of this method is simple implementation and high computational efficiency, making it suitable for real-time monitoring systems. For higher accuracy requirements, deep learning models (such as YOLO or CNN) can be incorporated for flame recognition.