农业机械学报2024，Vol.55Issue(11)：184-192,503,10.DOI:10.6041/j.issn.1000-1298.2024.11.020
基于轻量化MLCE-RTMDet的人工去雄后玉米雄穗检测算法
Maize Tassel Detection Algorithm after Artificial Emasculation Based on Lightweight MLCE-RTMDet
摘要
Abstract
Detecting missed tassels is crucial for assessing the quality of aritificial emasculation in maize seed production fields.Aiming at the problems of large parameter quantity,low detection efficiency and poor accuracy of the existing maize tassel detection models,a lightweight tassel detection model based on RTMDet-tiny,named MLCE-RTMDet,was proposed.The model used the lightweight MobileNetv3 as the feature extraction network to effectively reduce the model parameters.The CBAM attention module in the neck network was integrated to enhance multi-scale feature extraction capability for tassel objects,overcoming potential performance losses caused by the lightweight networks.Simultaneously,the EIOU Loss was adopted,replacing the GIOU Loss,which further improved the accuracy of tassel detection.Experiments on the self-built dataset showed that the improved MLCE-RTMDet model reduced model parameters to 3.9 × 106,while the number of floating point operations was lowered to 5.3 × 109,resulting in a 20.4％reduction in parameters and a 34.6％decrease in computational complexity compared with that of the original model.When evaluated on the test set,the model's mean average precision(mAP)reached 92.2％,reflecting a 1.2 percentage points improvement over the original model.The inference speed was increased to 41.9 frames per second(FPS),representing a 12.6％enhancement.Compared with current mainstream detection models such as YOLO v6,YOLO v8,and YOLO X,MLCE-RTMDet demonstrated superior overall detection performance.The improved high-accuracy lightweight model offered technical support for tassel re-inspection and emasculation quality assessment in maize seed production fields following artificial emasculation.关键词
无人机/目标检测/人工去雄/玉米雄穗/RTMDet/轻量化网络Key words
drone/object detection/artificial emasculation/maize tassel/RTMDet/lightweight network分类
信息技术与安全科学引用本文复制引用
李金瑞,杜建军,张宏鸣,郭新宇,赵春江..基于轻量化MLCE-RTMDet的人工去雄后玉米雄穗检测算法[J].农业机械学报,2024,55(11):184-192,503,10.基金项目
国家重点研发计划项目(2022YFD1900701)、黑龙江省"揭榜挂帅"科技攻关项目(20212XJ05A02)、北京市农林科学院科技创新能力建设专项(KJCX20230429)、国家自然科学基金项目(42377341)和陕西省重点研发计划项目(2023-YBNY-217) （2022YFD1900701）
