基于改进Ghost-YOLOv5s-BiFPN算法检测梨树花序

doi:10.12133/j.smartag.SA202207006

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 108-119.doi: 10.12133/j.smartag.SA202207006

• 专刊--智慧果园关键技术与装备 • 上一篇下一篇

基于改进Ghost-YOLOv5s-BiFPN算法检测梨树花序

夏烨^1,²(), 雷哓晖¹, 祁雁楠¹, 徐陶¹, 袁全春¹, 潘健¹, 姜赛珂¹, 吕晓兰¹()

^1.江苏省农业科学院农业设施与装备研究所/农业农村部园艺作物农业装备重点实验室，江苏南京 210014
^2.江苏大学农业工程学院，江苏镇江 210200

收稿日期:2022-07-14 出版日期:2022-09-30
基金资助:
江苏省现代农机装备与技术示范推广项目(NJ2022-14);财政部和农业农村部：国家现代农业产业技术体系资助;江苏省农业科技自主创新资金项目（CX（20）3058）;国家自然科学基金(32201680)
作者简介:夏烨（1998－），男，硕士研究生，研究方向为农业机器人和机器视觉。E-mail：misakuu@126.com
通信作者:

Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm

XIA Ye^1,²(), LEI Xiaohui¹, QI Yannan¹, XU Tao¹, YUAN Quanchun¹, PAN Jian¹, JIANG Saike¹, LYU Xiaolan¹()

^1.Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences / Key Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
^2.Institute of Agricultural Engineering Jiangsu University, Zhenjiang 210200, China

Received:2022-07-14 Online:2022-09-30

摘要/Abstract

摘要：

疏花是梨生产中的重要农艺措施，机械化智能疏花是当今高速发展的疏花方式，花朵与花苞的分类与检测是保证疏花机器正常工作的基本要求。本研究针对目前梨园智能化生产中出现的梨树花序检测与分类问题，提出了一种基于改进YOLOv5s的水平棚架梨园花序识别算法Ghost-YOLOv5s-BiFPN。通过对田间采集的梨树花苞与花朵图像进行标注与数据扩充后送入算法进行训练得到检测模型。Ghost-YOLOv5s-BiFPN运用加权双向特征金字塔网络（Bi-directional Feature Pyramid Network，BiFPN）替换原始的路径聚合网络（Path Aggregation Network，PAN）结构，对网络提取的不同尺寸目标特征进行有效的融合。同时运用Ghost模块替换传统卷积，在不降低准确度的同时减少模型参数量和提升设备运行效率。田间试验结果表明，改进的Ghost-YOLOv5s-BiFPN算法对梨树花序中花苞与花朵的检测精度分别为93.2%和89.4%，两种目标平均精度为91.3%，检测单张图像时间为29 ms，模型大小为7.62 M。相比于原始YOLOv5s算法，检测精度与召回度分别提升了4.2%和2.7%，检测时间和模型参数量分别降低了9 ms和46.6%。本研究提出的算法可对梨树花苞与花朵进行精确的识别和分类，为后续梨园智能化疏花的实现提供技术支持。

关键词: 梨树花序, 智能识别, YOLOv5s, 加权双向特征金字塔, 轻量化模型

Abstract:

Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning.

Key words: pear flower, intelligent recognition, YOLOv5s, BiFPN, lightweight model

中图分类号:

TP391.41

夏烨, 雷哓晖, 祁雁楠, 徐陶, 袁全春, 潘健, 姜赛珂, 吕晓兰. 基于改进Ghost-YOLOv5s-BiFPN算法检测梨树花序[J]. 智慧农业(中英文), 2022, 4(3): 108-119.

XIA Ye, LEI Xiaohui, QI Yannan, XU Tao, YUAN Quanchun, PAN Jian, JIANG Saike, LYU Xiaolan. Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm[J]. Smart Agriculture, 2022, 4(3): 108-119.

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基于改进Ghost-YOLOv5s-BiFPN算法检测梨树花序

Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm

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