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基于Laws與Gabor濾波的田間西蘭花花球識別技術
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浙江省重點研發(fā)計劃項目(2021C02021)和浙江省科技廳公益項目(LGN20E050006)


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

    正確識別西蘭花田間位置是實現(xiàn)西蘭花自動化采收的基礎,,西蘭花花球顏色與植株的葉片,、莖稈相似,僅通過顏色特征無法對西蘭花進行識別,本文以成熟期的田間西蘭花為研究對象,,提出了一種基于紋理特征與顏色特征的西蘭花識別算法,。首先通過預處理以及Laws濾波對圖像進行邊界紋理強化,再通過Gabor濾波對圖像進行紋理特征向量提取,,并對提取后的紋理特征向量進行z-score標準化,,隨后對標準化后的紋理特征向量進行K-means聚類與開運算,獲取花球潛在存在區(qū)域,。同時對RGB圖像進行HSV轉換,,通過對圖像的H分量進行閾值分割達到濾除地面像素的效果。最終對紋理特征識別與顏色特征識別的結果進行融合,,實現(xiàn)對田間西蘭花的識別,。算法通過結合紋理與顏色特征,對田間西蘭花進行了識別,,解決了西蘭花的花球與莖葉等背景顏色相近難以識別的問題,。本文共使用792幅圖像進行試驗,試驗結果表明,,本方法可以準確地對西蘭花田間圖像進行識別,,其精確率為96.96%,召回率為94.41%,,F(xiàn)1值為95.67%,。通過對3組不同拍攝環(huán)境的數(shù)據(jù)集進行算法識別,3組數(shù)據(jù)集的F1值始終保持在94%以上,,具有良好的拍攝環(huán)境適應性,,為農業(yè)機器人進行西蘭花自動化采收奠定了基礎,。

    Abstract:

    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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趙雄,徐港吉,陳建能,俞高紅,代麗.基于Laws與Gabor濾波的田間西蘭花花球識別技術[J].農業(yè)機械學報,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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  • 在線發(fā)布日期: 2022-08-10
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