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基于YOLO v7-ST模型的小麥籽粒計數(shù)方法研究
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中國農(nóng)業(yè)大學(xué)橫向課題項目(69193028)


Wheat Grain Counting Method Based on YOLO v7-ST Model
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    摘要:

    針對小麥考種過程中籽粒堆積,、粘連和遮擋現(xiàn)象導(dǎo)致計數(shù)準(zhǔn)確率低等問題,本文基于電磁振動原理設(shè)計了高通量小麥籽粒振動分離裝置,,通過分析受力探討了籽粒離散分離程度的主要影響因素,,并引入二階離散系數(shù)建立了籽粒離散度等級評價方法。在此基礎(chǔ)上,,引入Swin Transformer模塊構(gòu)建YOLO v7-ST模型,,對不同離散度等級下小麥籽粒進(jìn)行計數(shù)性能測試。試驗結(jié)果表明,,YOLO v7-ST模型在3種離散度等級下平均計數(shù)準(zhǔn)確率,、F1值和平均計數(shù)時間的總平均值分別為99.16%、93%和1.19s,,相較于YOLO v7,、YOLO v5和Faster R-CNN模型,平均計數(shù)準(zhǔn)確率分別提高1.03,、2.34,、15.44個百分點,模型綜合評價指標(biāo)F1值分別提高2,、3,、16個百分點,平均計數(shù)時間較YOLO v5和Faster R-CNN分別減少0.41s和0.36s,,僅比YOLO v7模型增大0.09s,。因此,YOLO v7-ST模型可實現(xiàn)多種離散度等級下不同程度籽粒遮擋和粘連問題的準(zhǔn)確快速檢測,,大幅提高小麥考種效率,。

    Abstract:

    Aiming at the problems of low counting accuracy due to seed accumulation, sticking and shading phenomena in the wheat seed testing process, a high-throughput wheat seed vibration separation device was designed based on the principle of electromagnetic vibration. The main influencing factors of the degree of seed dispersion and separation were discussed by analyzing the forces, and the secondorder dispersion coefficient was introduced to establish the seed dispersion grade evaluation method. On this basis, the YOLO v7-ST model was then built by using the Swin Transformer module and was tested for counting performance under different discrete degree levels. The experimental results showed that the mean counting accuracy, F1 value and mean counting time of the YOLO v7-ST model were 99.16%, 93% and 1.19s under the three dispersion levels, respectively. Compared with that of the YOLO v7, YOLO v5 and Faster R-CNN models, the mean counting accuracy was improved by 1.03 percentage points, 2.34 percentage points and 15.44 percentage points, respectively, and the F1 values of the comprehensive evaluation index of the model was increased by 2 percentage points, 3 percentage points and 16 percentage points, respectively. The mean counting time was decreased by 0.41s and 0.36s compared with that of YOLO v5 and Faster R-CNN, respectively, and it was only 0.09s slower than that of the YOLO v7 model. Overall, the YOLO v7-ST model provided accurate and efficient detection of grains under various discrete degree levels, significantly improved the efficiency of wheat breeding.

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王玲,張旗,馮天賜,王一博,李雨桐,陳度.基于YOLO v7-ST模型的小麥籽粒計數(shù)方法研究[J].農(nóng)業(yè)機(jī)械學(xué)報,2023,54(10):188-197,204. WANG Ling, ZHANG Qi, FENG Tianci, WANG Yibo, LI Yutong, CHEN Du. Wheat Grain Counting Method Based on YOLO v7-ST Model[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):188-197,204.

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  • 收稿日期:2022-12-02
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  • 在線發(fā)布日期: 2023-06-15
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