数字农科院2.0

YOUNG APPLE FRUITS DETECTION METHOD BASED ON IMPROVED YOLOv5

文献类型： 外文期刊

作者： Yonghui Du;Ang Gao;Yuepeng Song;Jing Guo;Wei Ma;Longlong Ren

作者机构：

关键词： CA attention mechanism;GIOU loss function;Prediction layer;YOLOv5;Young apple fruit

期刊名称： INMATEH - Agricultural Engineering

ISSN： 2068-4215

年卷期： 2024 年 73 卷 2 期

页码：

收录情况： EI（2024版）

摘要： The intelligent detection of young apple fruits based on deep learning faced various challenges such as varying scale sizes and colors similar to the background, which increased the risk of misdetection or missed detection. To effectively address these issues, a method for young apple fruit detection based on improved YOLOv5 was proposed in this paper. Firstly, a young apple fruits dataset was established. Subsequently, a prediction layer was added to the detection head of the model, and four layers of CA attention mechanism were integrated into the detection neck (Neck). Additionally, the GIOU function was introduced as the model's loss function to enhance its overall detection performance. The accuracy on the validation dataset reached 94.6%, with an average precision of 82.2%. Compared with YOLOv3, YOLOv4, and the original YOLOv5 detection methods, the accuracy increased by 0.4%, 1.3%, and 4.6% respectively, while the average precision increased by 0.9%, 1.6%, and 1.2% respectively. The experiments demonstrated that the algorithm effectively recognized young apple fruits in complex scenes while meeting real-time detection requirements, providing support for intelligent apple orchard management.

分类号：

  • 相关文献

[1]基于区域亮度自适应校正的茶叶嫩芽检测模型. 吕军,方梦瑞,姚青,武传宇,贺盈磊,边磊,钟小玉. 2021

[2]基于YOLOv5深度学习的茶叶嫩芽估产方法. 徐海东,马伟,谭彧,刘星星,郑永军,田志伟. 2022

[3]Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms. Peng Zhang,Zhichao Wang,Yuan Rao,Jun Zheng,Ning Zhang,Degao Wang,Jianqiao Zhu,Yifan Fang,Xiang Gao. 2023

[4]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[5]A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS. Wang C.,Sun S.,Zhao C.,Mao Z.,Wu H.,Teng G.. 2022

[6]Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image. Xu, Xingmei,Wang, Lu,Shu, Meiyan,Liang, Xuewen,Ghafoor, Abu Zar,Liu, Yunling,Ma, Yuntao,Zhu, Jinyu. 2022

[7]Deep-Learning-Based Rice Phenological Stage Recognition. Jiale Qin,Tianci Hu,Jianghao Yuan,Qingzhi Liu,Wensheng Wang,Jie Liu,Leifeng Guo,Guozhu Song. 2023

[8]Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method. Chen, Zhiwei,Chen, Jianneng,Li, Yang,Gui, Zhiyong,Yu, Taojie. 2023

[9]Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory. Xinfa Wang,Zhenwei Wu,Meng Jia,Tao Xu,Canlin Pan,Xuebin Qi,Mingfu Zhao. 2023

[10]An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s. Wang, Wenhao,Shi, Yun,Liu, Wanfu,Che, Zijin. 2024

[11]A Novel Deep Learning Method for Detecting Strawberry Fruit. Shuo Shen,Famin Duan,Zhiwei Tian,Chunxiao Han. 2024

[12]Small target tea bud detection based on improved YOLOv5 in complex background. Mengjie Wang,Yang Li,Hewei Meng,Zhiwei Chen,Zhiyong Gui,Yaping Li,Chunwang Dong. 2024

[13]A new training strategy: Coordinating distillation techniques for training lightweight weed detection model. Peng Zhou,Yangxin Zhu,Chengqian Jin,Yixiang Gu,Yinuo Kong,Yazhou Ou,Xiang Yin,Shanshan Hao. 2025

[14]Lightweight Structure and Attention Fusion for In-Field Crop Pest and Disease Detection. Zijing Luo,Yunsen Liang,Naimin Kong,Lirui Liang,Wenjun Peng,Yujie Yao,Chi Qin,Xiaohan Lu,Mingman Xu,Yining Zhang,Chenyang Lin,Chengyao Jiang,Mengyao Li,Yangxia Zheng,Yameng Jiang,Wei Lu. 2025

作者其他论文 更多>>