基于Laws与Gabor滤波的田间西兰花花球识别技术
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浙江省重点研发计划项目(2021C02021)和浙江省科技厅公益项目(LGN20E050006)


Field Broccoli Head Recognition Technology Based on Laws and Gabor Filter
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    摘要:

    正确识别西兰花田间位置是实现西兰花自动化采收的基础,西兰花花球颜色与植株的叶片、茎秆相似,仅通过颜色特征无法对西兰花进行识别,本文以成熟期的田间西兰花为研究对象,提出了一种基于纹理特征与颜色特征的西兰花识别算法。首先通过预处理以及Laws滤波对图像进行边界纹理强化,再通过Gabor滤波对图像进行纹理特征向量提取,并对提取后的纹理特征向量进行z-score标准化,随后对标准化后的纹理特征向量进行K-means聚类与开运算,获取花球潜在存在区域。同时对RGB图像进行HSV转换,通过对图像的H分量进行阈值分割达到滤除地面像素的效果。最终对纹理特征识别与颜色特征识别的结果进行融合,实现对田间西兰花的识别。算法通过结合纹理与颜色特征,对田间西兰花进行了识别,解决了西兰花的花球与茎叶等背景颜色相近难以识别的问题。本文共使用792幅图像进行试验,试验结果表明,本方法可以准确地对西兰花田间图像进行识别,其精确率为96.96%,召回率为94.41%,F1值为95.67%。通过对3组不同拍摄环境的数据集进行算法识别,3组数据集的F1值始终保持在94%以上,具有良好的拍摄环境适应性,为农业机器人进行西兰花自动化采收奠定了基础。

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    Correctly identifying the field location of broccoli is the basis for realizing automatic harvesting of broccoli. Because the flower ball color is similar to the plant stem, broccoli cannot be identified only by color features. The algorithm firstly strengthened the boundary texture of the image through pretreatment and Laws filter, in which the filter kernel function of Laws adopted E5×E5. Then Gabor filter was applied to the texture enhanced image, and Gabor transform which was a short-time window Fourier transform proposed to meet the locality of two dimensional images in spatial and frequency domain, with window function of Gaussian function. Through Gabor filter, each pixel had a 1×8 dimensions texture feature vector, which was generated by eight different Gabor filtering kernel functions that were determined by the wavelengths of one sinusoidal modulation wave and the directions of eight different kernel functions. The texture feature vector was zero-mean normalization to speed up the convergence of clustering process, and K-means clustering segmentation and open operation were performed to obtain the potential region of broccoli heads. Meanwhile, the image was segmented based on color features. Through converting RGB (red, green, blue) image into HSV (Hue, Saturation, Value) image, the Hue component of the image was threshold to filter out ground pixels. Finally, the results of texture feature recognition and color feature recognition were fused to realize the recognition of field broccoli heads. A total of 792 images were used for the experiment. The experimental results showed that this method could accurately identify the broccoli field images. The precision rate was 96.96%, the recall rate was 94.41%, and the F1 score was 95.67%. Through the algorithm recognition of three sets of different shooting environment data sets, the F1 score of the three sets of data sets was always maintained at more than 94%, which had good shooting environment adaptability and laid a foundation for automatic harvesting of broccoli by agricultural robots.

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赵雄,徐港吉,陈建能,俞高红,代丽.基于Laws与Gabor滤波的田间西兰花花球识别技术[J].农业机械学报,2023,54(4):313-322. ZHAO Xiong, XU Gangji, CHEN Jianneng, YU Gaohong, DAI Li. Field Broccoli Head Recognition Technology Based on Laws and Gabor Filter[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(4):313-322.

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  • 收稿日期:2022-07-17
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  • 在线发布日期: 2022-08-10
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