CFAR Detection Algorithm Implementation Code

This resource provides a comprehensive implementation of a CFAR (Constant False Alarm Rate) detection algorithm, designed to support your work in image processing and computer vision applications. The code features a well-structured implementation that clearly demonstrates the core CFAR algorithm, including threshold calculation methods and noise estimation techniques. Key functions include adaptive threshold determination based on surrounding cell statistics, guard cell handling to prevent target self-masking, and configurable false alarm rate parameters. The implementation follows standard CFAR variants (CA-CFAR, OS-CFAR) with modular design for easy customization. Through this code, you can gain practical understanding of target detection and tracking mechanisms in image analysis, with commented sections explaining mathematical formulations and optimization approaches. The code includes example applications demonstrating detection performance on sample datasets. Should you require technical clarification or have suggestions for enhancement, please feel free to contact me for further discussion.