基于改进YOLOv7一tiny的烟叶主脉轻量化检测研究

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中图分类号：S238 文献标识码：A 文章编号：2095-5553（2025）07-0138-07

Abstract：Inordertoaccuratelyidentifythemainveinoftobacoleaf，realizemechanicalgraspingandreducetherateof grasping damage，animprovedlightweightobaccoleaf mainveinsrecognitionmodelbasedonYOLOv7—tinywasproposedFirstly， theoriginaltrunk featureextractionnetwork isreplacedbyamore lightweight MobileNetV3basedonYOLOv7—tinynetwork， thedefaulth-swishactivationfunctioninthemoduleisreplacedbyReLUactivationfunction.Then，thecommonconvolutionof theneckisreplacedbyalightweightGSConvandaSlim—Neckdesignisadoptedtoompressthechannelofthemodelandeliminate theredundantfeatureredundancyinordertolightenthenetworkstructure.Atlast，theSIoUlossfunctionwas introducedtoreduce thelossvalueofthemodelandenhancethefusionabilityof themodeltothemainveinoftobaco.Theresultsshowedthatthemap value of the improved model on the tobacco leaf dataset was 91.3% ，at a cost of only 1.6% loss，the parameter quantity was reduced by 51.1% compared with the original model，and the computational load was 4.3G ，only 32.6% of the original model （13.2G）. Compared with YOLOv5—s （ 16.5G ，YOLOv6—n（11.4G），Yolox—s （26.8G），YOLOv8—n（8.7G），and YOLOv9—t （7.7G），allofthemwereimproved.Theimprovedmodelcanbedeployedinthemarginalequipmentwithscarcecomputing resources，which provides some technical support for the mechanized harvesting of tobacco leaves.

Keywords：tobacco leaf main veins；lightweight；mechanized harvesting；accurate identification；marginal deployment

0 引言

有重要影响。（剩余12308字）