基于HED的四边形检测系统的研究及实现

Research and Implementation of Quadrilateral Detection System Based on HED

四边形检测是图像处理和计算机视觉中的基础问题,它是许多更复杂形状检测和识别任务的基础。深度学习在图像识别和分类中已取得了很大的成功,但如何将其与传统的图像处理方法结合,以提高四边形检测的效率和准确性,仍是值得研究的问题。简述了自然场景中一种基于HED的四边形检测方法,首先使用灰度处理和二值化处理进行初步处理;其次对图像进行膨胀边缘并利用OpenCV库画出图片的轮廓;再次对图像求取凸包并利用多边形逼近的方法对轮廓进行噪声过滤;最后结合HED算法优化提取的轮廓进行矩阵判定。结果表明,该系统对四边形检测具有良好的效果。

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.