An improved image segmentation model was established to achieve accurate detection of target contours under high noise, low resolution, and uneven illumination environments. The new model is based on the variational level set algorithm, which improves the C–V (Chan and Vese) model and GAC (Geodesic Active Contour) model, fuses the contour and area models to segment the image information, that is, the edge information and region information of the image are fused into the same "energy" functional. According to the geometric characteristics of the curve, GAC model can effectively avoid re parameterization and light insensitivity in the evolution process, and CV model can effectively distinguish the fuzzy boundary of the image by maximizing the gray difference between the target and the background, it has strong anti-noise performance. By solving the steady-state solution of the partial differential equation, the optimal solution of the energy model is solved. New method can improve the calculation accuracy, topological structure adaptability, anti-noise ability, and reduce the light sensitivity effectively. Experiment shows that the new model has good robustness, high real-time performance, and it can effectively improve detection accuracy.

建立改进的图像分割模型，实现高噪声、低分辨率、不均匀光照环境下目标轮廓的准确检测。新模型基于变分水平集算法，改进了C-V（Chan and Vese）模型和GAC（Geodesic Active Contour）模型，融合了轮廓和区域模型来分割图像信息，即边缘图像的信息和区域信息融合成同一个“能量”函数。根据曲线的几何特征，GAC模型可以有效避免进化过程中的重新参数化和光不敏感，CV模型可以通过最大化目标与背景的灰度差异来有效区分图像的模糊边界，具有抗噪性能强。通过求解偏微分方程的稳态解，求解能量模型的最优解。新方法可以提高计算精度、拓扑结构适应性、抗噪声能力，并有效降低光敏度。实验表明，新模型鲁棒性好，实时性高，能有效提高检测精度。