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基于YOLO v5s的自然場(chǎng)景油茶果識(shí)別方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2019YFD1002401)和國(guó)家自然科學(xué)基金項(xiàng)目(31701326)


Camellia oleifera Fruit Detection in Natural Scene Based on YOLO v5s
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    摘要:

    針對(duì)油茶果體積小,、分布密集、顏色多變等特點(diǎn),,為實(shí)現(xiàn)自然復(fù)雜場(chǎng)景下油茶果的快速精準(zhǔn)定位,,并依據(jù)果實(shí)的疏密分布,確定恰當(dāng)?shù)淖詣?dòng)振蕩采收裝置夾持位置,,利用YOLO v5s卷積神經(jīng)網(wǎng)絡(luò)模型,,開展了自然環(huán)境下油茶果圖像檢測(cè)方法研究,用3296幅油茶果圖像制作PASCAL VOC的數(shù)據(jù)集,,對(duì)網(wǎng)絡(luò)進(jìn)行了150輪訓(xùn)練,,得到的最優(yōu)權(quán)值模型準(zhǔn)確率為90.73%,召回率為98.38%,,綜合評(píng)價(jià)指標(biāo)為94.4%,,平均檢測(cè)精度為98.71%,單幅圖像檢測(cè)時(shí)間為12.7ms,,模型占內(nèi)存空間為14.08MB,。與目前主流的一階檢測(cè)算法YOLO v4-tiny和RetinaNet相比,其精確率分別提高了1.99個(gè)百分點(diǎn)和4.50個(gè)百分點(diǎn),,召回率分別提高了9.41個(gè)百分點(diǎn)和10.77個(gè)百分點(diǎn),,時(shí)間分別降低了96.39%和96.25%,。同時(shí)結(jié)果表明,該模型對(duì)密集,、遮擋,、昏暗環(huán)境和模糊虛化情況下的果實(shí)均能實(shí)現(xiàn)高精度識(shí)別與定位,具有較強(qiáng)的魯棒性,。研究結(jié)果可為自然復(fù)雜環(huán)境下油茶果機(jī)械采收及小目標(biāo)檢測(cè)等研究提供借鑒,。

    Abstract:

    In view of the characteristics of small size, dense distribution and changeable color of Camellia oleifera fruit, in order to realize the rapid and accurate identification of Camellia oleifera fruit in complex natural scene, and determine the appropriate clamping position for the automatic oscillating harvesting device according to the density distribution of the fruit, the YOLO v5s convolutional neural network model was used to carry out research on the image detection method of Camellia oleifera fruit in the natural scene. Through data enhancement, totally 3296 Camellia oleifera fruit images were obtained to make the PASCAL VOC data set. After 150 rounds of training, the optimal weight model was got. The accurate rate was 90.73%, the recall rate was 98.38%, the comprehensive evaluation index was 94.4%, the average detection accuracy was 98.71%, the single image detection time was 12.7ms, and the memory size of the model was 14.08MB. Compared with the current mainstream first-stage detection algorithms YOLO v4-tiny and RetinaNet, its accuracy rate was increased by 1.99 percentage points and 4.50 percentage points, the recall rate was increased by 9.41 percentage points and 10.77 percentage points, and the time was reduced by 96.39% and 96.25%, respectively. In addition, the weight file of the YOLO v5s model was small, indicating that its network was simpler and had the advantage of rapid deployment. It could be transplanted to edge devices in the future to provide algorithm reference for the vision system of the Camellia oleifera fruit automatic harvesting device. Through comparative experiment, the results also showed that the model can achieve high-precision recognition and positioning of fruits in dense, occluded, dim environments and fuzzy blur conditions, and it had strong robustness. The research results can provide a reference for the research of mechanical harvesting of Camellia oleifera fruit under the natural complex environment.

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宋懷波,王亞男,王云飛,呂帥朝,江梅.基于YOLO v5s的自然場(chǎng)景油茶果識(shí)別方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2022,53(7):234-242. SONG Huaibo, WANG Ya’nan, WANG Yunfei, Lü Shuaichao, JIANG Mei. Camellia oleifera Fruit Detection in Natural Scene Based on YOLO v5s[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(7):234-242.

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  • 收稿日期:2021-07-27
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  • 在線發(fā)布日期: 2022-07-10
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