基于免疫蚁群融合算法的机械臂目标图像边缘检测

Image edge detection method based on immune ant colony algorithm for manipulator target

针对工业机械臂目标自识别所需的高效图像边缘检测要求,提出了一种基于免疫蚁群融合算法的图像边缘检测方法。该方法以信息素为基准,通过注射疫苗进行免疫选择从而优化启发信息,提高后续遍历效率并有效缩短检测时间。避免了蚁群在游历全图时可能产生的局部最优、收敛停滞等问题,从而得到符合机械臂自识别要求的图像处理结果,并能够较好地提高迭代遍历效率,缩短后续处理时间。仿真实验结果表明,该算法能够得到较好的边缘检测结果。

According to the requirements of efficient image edge detection for the manipulator self-recognition,this paper proposed a method of image edge detection based on improved fusion algorithm.In order to avoid detection errors by local optimal solution and the stagnation of convergence,ant colony algorithm combined with immune algorithm were taken to traversing the image,which used pheromone as standard.Further,immunization selection through vaccination optimized the heuristic information,then it improved the efficiency of ergodic process,and shortened the time of detection effectively.And the results of image processing was suitable for the manipulator self-recognition.Simulation and experimental of image edge detection result shows that this algorithm is effective with manipulator target images.