基于改進(jìn)YOLOv8的檸檬果實(shí)識(shí)別方法

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DOI： 10.13718/j. cnki. xdzk. 2025. 07.019

關(guān)鍵詞：檸檬識(shí)別；YOLOv8；SPD卷積模塊；Wise-IoU損失函數(shù)；注意力機(jī)制

中圖分類(lèi)號(hào)：TP391；S23 文獻(xiàn)標(biāo)識(shí)碼：A

文章編號(hào)：1673-9868（2025）07-0219-12

A Lemon Fruit Recognition Method Based on Improved YOLOv8

LIU Yucheng， LIANG Xincheng， LI Falin， ZHANG Fengling， LI Yunwu College of Engineering and Technology，Southwest University，Chongqing 400715，China

Abstract：In order to address the challenges of high costs and low efficiency of manual picking lemon fruits，and achieve swift and precise identification of lemon fruits in intricate environments，a lemon fruit recognition method based on the improved YOLOv8 model was established. Firstly，the SPDConv module was introduced into the backbone network to enhance the accuracy of model's detection for low-resolution images and small targets. Then，the EMA attention mechanism was added to effectively extract the features of obscured fruits. Finally，the CIoU bounding box lossfunction was replaced with Wise-IoU to reduce the dependence on high-quality anchor boxes and improve the generalization ability of the model. Tested on a self-constructed dataset，the YOLOv8-SEW model exhibited precision，recalland mean average precision values of 94.5% ， 85.7% and 92.4% separately. Compared with before improvement，the precision，recall and mean average precision of the model was increased by 1.0% ， 4.2% and 2.9% ，respectively. The detection time for a single image was 44.8ms ， enabling rapid and accurate identification of lemon fruits，thus providing a technological foundation for automatic harvesting lemon fruits.

Key words： recognition of lemon fruits; YOLOv8; SPD convolution module; Wise-IoU loss function; attention mechanism

丘陵山區(qū)的檸檬因其皮厚氣香、出汁率高而受到市場(chǎng)的廣泛歡迎，在農(nóng)業(yè)和食品行業(yè)中具有重要的經(jīng)濟(jì)價(jià)值和市場(chǎng)需求[-2]。（剩余13366字）