基于全景视觉的智能农业车辆运动障碍目标检测

为了满足智能农业车辆安全正常作业，提出了基于全景视觉的运动障碍目标检测。与传统的单目和双目视觉相比，全景视觉具有360°无盲区检测的优点。首先系统使用多线程技术采集多目视觉图像，并用改进RANSAC-SIFT算法进行特征点提取与匹配，进而拼接全景视觉图像；其次采用改进的CLG光流法处理全景图像，检测运动障碍目标。试验表明：基于多线程技术和改进RANSAC-SIFT的全景拼接算法，与传统SIFT算法相比，平均提高特征点匹配准确度25.6%，加快运算速度25.0%；采用改进CLG光流法进行运动障碍检测，平均检测时间为1.55 s，检测成功率为95.0%。

Moving Obstacle Detection Based on Panoramic Vision for Intelligent Agricultural Vehicle

In order to satisfy the safety and normal operation for intelligent agricultural vehicle, a method of detecting moving obstacles was proposed based on panoramic vision. Compared with the traditional monocular and binocular vision, panoramic vision possessed the advantages of 360° non-blind area detection. Firstly, multi-thread technology was used to acquire multi-vision images. The improved RANSAC-SIFT algorithm was used to extract and match feature points, and then stitch panoramic images. Secondly, improved CLG optical flow algorithm was used to detect moving obstacles based on panoramic images. Compared with the traditional SIFT algorithm , experiments showed that the accuracy of feature points matching was increased by 25.6% and the arithmetic speed was increased by 25.0%. Moving obstacle detection using improved CLG optical flow algorithm could take averagely 1.55 s to detect moving obstacles, and the accuracy was 95.0%.