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基于改進(jìn)YOLO v5s的甘蔗切種莖節(jié)特征識別定位技術(shù)
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國家自然科學(xué)基金項目(52165009)


Stem Node Feature Recognition and Positioning Technology for Transverse Cutting of Sugarcane Based on Improved YOLO v5s
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    摘要:

    為了實現(xiàn)甘蔗智能橫向切種工作站的精準(zhǔn),、高效的自動化切種,,針對工廠化切種任務(wù)的特點,提出了一種基于改進(jìn)YOLO v5s的甘蔗莖節(jié)特征邊緣端識別與定位方法,。首先,,利用張正友相機(jī)標(biāo)定法對攝像頭進(jìn)行畸變矯正;然后對甘蔗莖節(jié)數(shù)據(jù)集進(jìn)行數(shù)據(jù)增強(qiáng),,利用原始的YOLO v5s模型進(jìn)行訓(xùn)練和測試,,結(jié)果顯示數(shù)據(jù)增強(qiáng)能一定程度上提高檢測精度。針對莖節(jié)特征目標(biāo)小以及模型體積大導(dǎo)致檢測精度低,、部署難度高等問題,,對YOLO v5s的骨干網(wǎng)絡(luò)進(jìn)行改進(jìn),在SPPF特征融合模塊前引入坐標(biāo)注意力(Coordinate attention,,CA)模塊和Ghost輕量化結(jié)構(gòu),,在Head網(wǎng)絡(luò)中剔除P5大目標(biāo)檢測頭,得到了改進(jìn)后甘蔗莖節(jié)檢測模型YOLO v5s-CA-BackboneGhost-p34,,測試結(jié)果表明該模型優(yōu)于其他主流算法和原始模型,,具有高精度,、小體積等優(yōu)勢,。其中,平均精度均值1和平均精度均值2分別提高5.2,、16.5個百分點,,模型浮點數(shù)計算量和內(nèi)存占用量分別降低42%和51%。最后,,為了提高檢測速度和實時性,,將模型部署于邊緣端,利用TensorRT技術(shù)加快檢測速度,,并在傳送速度為0.15m/s的甘蔗智能橫向切種工作站上完成實際切種實驗,。實驗結(jié)果表明,加速后莖節(jié)檢測速度達(dá)到95f/s,,實時檢測定位平均誤差約為 2.4mm,,切種合格率為100%,漏檢率0.4%,,說明本文提出的模型具有高度可靠性和實用性,,可以為甘蔗橫向切種工作站的工廠化,、智能化以及標(biāo)準(zhǔn)化應(yīng)用提供有效的技術(shù)支持。

    Abstract:

    In order to achieve accurate and efficient automated seed cutting in sugarcane intelligent transverse seed cutting workstation, a method based on improved YOLO v5s for identifying and locating the edge end of sugarcane stem node features was proposed for the characteristics of factory seed cutting tasks. Firstly, the camera was corrected for distortion by using the ZHANG Zhengyou camera calibration method, then the sugarcane stem node dataset was enhanced and the original YOLO v5s model was used for training and testing, and the results showed that the data enhancement can improve the detection accuracy to some extent. Then, to address the problems of low accuracy and high model complexity caused by small stem node feature targets, the backbone network of YOLO v5s was improved by introducing the coordinate attention module and Ghost lightweight structure before the SPPF module, and removing the P5 large target detection head in the Head network to obtain the improved sugarcane stem node detection model YOLO v5s-CA-BackboneGhost-p34. The test results showed that the model outperformed other mainstream algorithms and the original model with high accuracy and small size. Among them, [email protected] and [email protected]∶0.95 were improved by 5.2 and 16.5 percentage points, respectively, and the model computation and size were reduced by 42% and 51%, respectively. Finally, in order to improve the detection speed and real-time performance, the model was deployed at the edge end, and the detection speed was accelerated by using TensorRT technology, and the model was completed on a sugarcane with transmission speed of 0.15m/s. The actual seed cutting test were completed on the smart transverse seed cutting workstation with transmission speed of 0.15m/s. The test results showed that the accelerated stem node detection speed reached 95f/s, the average error of real-time detection and positioning was about 2.4mm, the seed cutting qualification rate was 100%, and the leakage rate was 0.4%, which indicated that the model proposed was highly reliable and practical, and can provide effective technical support for the industrialization, intelligence and standardization of sugarcane transverse seed cutting workstation.

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李尚平,鄭創(chuàng)銳,文春明,李凱華,甘偉光,李洋.基于改進(jìn)YOLO v5s的甘蔗切種莖節(jié)特征識別定位技術(shù)[J].農(nóng)業(yè)機(jī)械學(xué)報,2023,54(10):234-245,,293. LI Shangping, ZHENG Chuangrui, WEN Chunming, LI Kaihua, GAN Weiguang, LI Yang. Stem Node Feature Recognition and Positioning Technology for Transverse Cutting of Sugarcane Based on Improved YOLO v5s[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):234-245,,293.

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  • 收稿日期:2023-05-30
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  • 在線發(fā)布日期: 2023-07-10
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