基于改进遗传算法的农机具视觉导航线检测

针对机器视觉导航系统现有导航线提取算法检测速度慢、抗干扰性差等不足,提出一种基于改进遗传算法(IGA)的导航线检测方法。图像中作物行走向近似为一条直线,从图像顶边和底边分别随机选一个点进行染色体编码,通过遗传进化选择适应度最高的个体作为作物行直线编码,进而得到导航线。改进遗传算法采用概率保留法和最优保存策略相结合的方法作为选择算子,提高了算法的搜索效率和精度;通过自适应调整交叉概率和变异概率,提高了算法的收敛速度和全局搜索能力。动态导航跟踪试验表明,改进的遗传算法与标准霍夫变换、标准遗传算法(GA)在导航线提取性能上相比,具有抗干扰性强、检测速度快等优点。当导航速度为0.6m/s时,横向偏差最大值不超过76 mm,平均值小于33.1 mm,较好地满足了导航作业要求。

Navigation Line Detection for Farm Machinery Based on Improved Genetic Algorithm

Aimed at the problem of low processing speed and poor anti-interference in existing algorithms for the visual navigation, an algorithm based on improved genetic algorithm (IGA) was designed for navigation line detection. Since the trend of crop row in image was approximate to a line, two points from the bottom and top sides of image were randomly selected to code as chromosome. By multiple genetic evolutions, the highest fitness individual was chosen as the crop row line, so as to obtain the navigation line. To increase search efficiency and accuracy, IGA adopted the method of combination of probability preservation and optimal preservation strategy as the selection operator. The probabilities of crossover and mutation were adjusted automatically to improve the convergence speed and global search ability. The experimental results showed that, compared with Hough transform and Generic algorithm, IGA had the advantages of rapid detection and strong anti-interference. When the velocity of the vehicle was 0.6m/s, the maximum lateral deviation and average lateral deviation were 76 mm and 33.1 mm respectively, which could meet the requirement of farm machinery navigation.