Authors

Abstract

In order to solve the problem of difficult pre-processing of crop video image shadows, a probable learning pixel classification method is proposed to study its processing technology. The algorithm effectively detects the shadow area by performing intelligent video collaborative detection on the shaded parts of the crop video sequence. Firstly, the cloud collaborative detection algorithm that can be widely used in agriculture was proposed. The video key frame was obtained and the background modeling algorithm with strong adaptability to crop illumination was applied to realize real-time detection of the target, so as to construct the crop pixel model. Finally, the proposed algorithm and the constructed model are applied to the processing of shadows of agricultural plant video images for experimental verification. The results show that in video frames 47, 194 and 258, the probable learning pixel classification method can be used to determine the shaded part of each frame, which can greatly improve the detection accuracy of crop shadows. The research in this paper shows that the probability learning pixel classification method can better enhance the shadow robustness and accuracy of crop video images.

Abstract in Chinese

为了解决作物视频图像阴影预处理困难的问题，提出了一种概率学习像素分类方法来研究其处理技术。该算法通过对作物视频序列的阴影部分进行智能视频协同检测，有效地检测出阴影区域。首先，提出了可广泛应用于农业领域的云协同检测算法。获取视频关键帧，采用对作物光照适应性强的背景建模算法实现对目标的实时检测，从而构建作物像素模型。最后，将所提出的算法和所构建的模型应用于农业植物视频图像的阴影处理并进行实验验证。结果表明，在47帧、194帧和258帧中，采用概率学习像素分类方法可以确定每帧的阴影部分，大大提高了作物阴影的检测精度。本文的研究表明，基于概率学习的像素分类方法能够更好地加强作物视频图像阴影的稳定性和准确性