基于小波多分辨率分解的农田障碍物检测

Detection of Obstacles in Farmland Based on Wavelet Multi-resolution Transform

针对基于颜色或高度信息的农田障碍物检测方法仅能实现部分障碍物检测的缺点,提出了基于频率信息的检测方法。采用小波多分辨率分解,利用田间作物产生主频信息的总量优势及作物行分布规律确定作物所在频率层。在作物层上利用图像旋转投影法校正图像的同时,获得航位偏差和航向偏差;依据频率分布特性的改变,检测出发生行遮挡的疑似障碍物位置;依据非杂草类障碍物频率变化比较缓慢,在小波多分辨率分解的最高频率层上实现不发生作物行遮挡的疑似障碍物的检测;最后采用立体视觉匹配及频率信息的先验知识判定检测到的是否为障碍物。实验表明算法能检测出包括长满草的土堆、田头等各类障碍物,并能有效去除断垄干扰,单帧图像处理时间平均为79 ms。

Since the obstacle detection methods based on color and height information could only detect some of all obstacles in farmland, a detection method based on frequency was proposed. The wavelet multi-resolution decomposition was developed to find the frequency layer of crops and it was observed that the total frequency of crops was more dominant than others and the distribution of crops row were considered. Then the positions and horizontal dimension of possible obstacles crossed the crop rows in the image were detected based on frequency distribution of the selection layer. The others could be detected in the highest frequency layer due to the lower frequency of this kind of obstacles. Then the stereo rectification and the prior frequency knowledge of obstacles were adopted to confirm if the detection was obstacle. The experiment showed that the proposed method could detect the mound, the edge of farmland and other obstacles effectively. The average time of processing each frame was 79 ms.