ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

Reliable and accurate visual detection of crop rows is prerequisite for implementing successful autonomous navigation for plant protection robots. A visual navigation path detection approach based on random sample consensus (RANSAC) algorithm was proposed. Firstly, the excess green (ExG) method and the maximum variance between classes were used to figure out gross target regions. Secondly, morphological operations and dynamic area threshold filtering strategy were employed to filter out the interferences. As outlier points significantly influenced the estimation accuracy, RANSAC algorithm was proposed to purify the inlier point sets. Finally, crop rows line features were modelled by least mean square techniques, which offered a degree of robustness in constructing global co-linear features in contrast to Hough transformation. To sufficiently verify the effectiveness of the idea, wheat, peanut, corn and film covered potato seedling images were used for evaluation. As revealed by experimental results that the proposed method outperformed Hough transformation in the crop rows center line extraction, and RANSAC algorithm rendered the method more robust with respect to noise and outliers, which allowed the successful detection rate of the work to be improved by 18.8 percentage points and arrived at 93.8%. The overall framework made sense to reliable visual navigation for plant protection robots.