Research and Implementation of Quadrilateral Detection System Based on HED
Quadrilateral detection is a fundamental problem in image processing and computer vision,serving as the foundation for many more complex shape detection and recognition tasks.While deep learning has achieved significant success in image recognition and classification,integrating it with traditional image processing methods to improve the efficiency and accuracy of quadrilateral detection remains a worthwhile research endeavor.This paper presents a method for quadrilateral detection in natural scenes based on HED.The approach involves initial preprocessing steps such as grayscale and binarization,followed by dilation of edges and contour drawing using the OpenCV library.Subsequently,convex hull computation and polygonal approximation are employed for noise filtering on the contours.Finally,integrating the HED algorithm optimizes the extracted contours and performs matrix-based decision making.The results show that this system in quadrilateral detection,showcasing practical applicability and promising outcomes.
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