改进YOLOv8的步进式烤房烟叶烘烤阶段识别方法

doi:10.14088/j.cnki.issn0439-8114.2025.09.032

湖北农业科学 ›› 2025, Vol. 64 ›› Issue (9): 202-212.doi: 10.14088/j.cnki.issn0439-8114.2025.09.032

改进YOLOv8的步进式烤房烟叶烘烤阶段识别方法

王春清, 尚书旗, 张茜雅, 刘伟, 岳丹松

1.青岛农业大学机电工程学院,山东青岛 266109;
2.山东源泉机械有限公司,山东临沂 276400

收稿日期:2025-01-26 出版日期:2025-09-25 发布日期:2025-10-28
通讯作者: 岳丹松（1978-）,男,山东潍坊人,副教授,博士,主要从事农业机械化、自动化控制、图像处理等研究,（电话）13573820687（电子信箱）200501042@qau.edu.cn。
作者简介:王春清（1998-）,男,山东潍坊人,硕士,主要从事新型智能农机装备与烟叶烘烤方向的计算机视觉研究,（电话）15689188043（电子信箱）wangchunqing1006@163.com。
基金资助:
山东省重点研发计划（重大科技创新工程）项目（2022CXGC010611）

Recognition method for tobacco leaves curing stages in step-type curing barns using improved YOLOv8

WANG Chun-qing, SHANG Shu-qi, ZHANG Xi-ya, LIU Wei, YUE Dan-song

1. College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China;
2. Shandong Yuanquan Machinery Co., Ltd., Linyi 276400, Shandong, China

Received:2025-01-26 Published:2025-09-25 Online:2025-10-28

摘要/Abstract

摘要： 针对步进式烤房环境下的烟叶烘烤阶段识别需求,提出一种基于改进YOLOv8的实时检测模型T-YOLOv8。首先,引入轻量化的EfficientViT作为主干网络,以提升模型在高复杂场景中的检测精度和推理速度。其次,通过设计可变形自适应注意力机制模块（Deformable adaptive attention mechanism, DA-Attention）,增强模型在不同环境条件下的特征融合与表达能力,进一步提升多样化数据输入下的鲁棒性。最后,融入跨阶段特征提取模块（CSPStage）和改进的损失函数(Focal EIoU_loss),优化目标特征的提取与融合效率,同时降低模型计算成本。结果表明,T-YOLOv8模型的精确度、召回率和平均精度（mAP50）分别达90.3%、94.1%和95.2%,较YOLOv8模型分别提高5.2%、4.1%和5.2%。相较于YOLO系列模型及经典目标检测模型,T-YOLOv8模型在实时性和准确性方面具有明显优势。T-YOLOv8模型实现了步进式烤房烟叶烘烤状态的实时监测,可支持智能化和自动化烘烤系统的构建。

关键词: 改进YOLOv8, 步进式烤房, 烟叶烘烤, 识别方法

Abstract: To address the need for recognizing tobacco leaves curing stages in step-type curing barns, a real-time detection model named T-YOLOv8 based on improved YOLOv8 was proposed. First, a lightweight EfficientViT was introduced as the backbone network to enhance the detection accuracy and inference speed of the model in highly complex scenarios. Second, by designing a deformable adaptive attention mechanism module (DA-Attention), the model’s feature fusion and expression capabilities under different environmental conditions were enhanced, further improving its robustness with diverse data inputs.Finally, a cross-stage feature extraction module (CSPStage) and an improved loss function (Focal EIoU_loss) were incorporated to optimize the efficiency of target feature extraction and fusion while reducing the computational cost of the model. The results showed that the precision, recall, and mean average precision (mAP50) of the T-YOLOv8 model reached 90.3%, 94.1%, and 95.2%, respectively, representing improvements of 5.2%, 4.1%, and 5.2% compared to the YOLOv8 model. Compared to the YOLO series models and classical object detection models, the T-YOLOv8 model demonstrated significant advantages in real-time performance and accuracy. The T-YOLOv8 model achieved real-time monitoring of tobacco leaves curing status in step-type curing barns, providing support for the construction of intelligent and automated curing systems.

Key words: improved YOLOv8, step-type curing barns, tobacco leaves curing, recognition method

中图分类号:

TP391

王春清, 尚书旗, 张茜雅, 刘伟, 岳丹松. 改进YOLOv8的步进式烤房烟叶烘烤阶段识别方法[J]. 湖北农业科学, 2025, 64(9): 202-212.

WANG Chun-qing, SHANG Shu-qi, ZHANG Xi-ya, LIU Wei, YUE Dan-song. Recognition method for tobacco leaves curing stages in step-type curing barns using improved YOLOv8[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(9): 202-212.

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改进YOLOv8的步进式烤房烟叶烘烤阶段识别方法

Recognition method for tobacco leaves curing stages in step-type curing barns using improved YOLOv8

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