改进伪标签半监督方法的石榴生长阶段检测模型

doi:10.3778/j.issn.1002-8331.2403-0355

摘要/Abstract

摘要： 精准检测果实的生长阶段对于提高农业机器人的作业效率至关重要。然而，传统的全监督目标检测模型依赖于大量的数据标注，实现成本过高。鉴于此，提出了一种利用少量标注数据训练的目标检测模型。包括：（1）采用YOLOv8构建教师-学生模型，负责生成伪标签与训练网络；（2）通过改良的伪标签分配方法，筛选出可靠、不确定与不可靠的伪标签，减少低质量伪标签对学生网络训练的负面影响；（3）针对这三种伪标签优化了训练损失函数，以更好地提取不同置信度的伪标签内的潜在信息。通过在石榴生长阶段数据集上进行实验验证，结果显示，在仅使用10%的标注数据的情况下，所提方法的平均精度达到了90.8%，比同样数据集下的YOLOv8网络提升了5.5个百分点，且该性能已接近于使用全标注数据训练的YOLOv8的92%。这一结果充分证明了提出的方法在标注资源有限的背景下，依然能够训练出高效且准确的目标检测模型。

关键词: 半监督, 伪标签, 目标检测, 师生模型

Abstract: Accurate detection of fruit growth stages is crucial for improving the operational efficiency of agricultural robots. However, traditional fully supervised object detection models rely on a large amount of data labeling, which is too costly to implement. In view of this, this study proposes a object detection model trained using a small amount of labeled data. It includes： (1) constructing a teacher-student model using YOLOv8, which is responsible for generating pseudo-labeling and training the network; (2) filtering out reliable, uncertain and unreliable pseudo-labels through an improved pseudo-label assignment method to reduce the negative impact of low-quality pseudo-labels on the training of student networks; (3) optimizing the training loss function for these three kinds of pseudo-labels to better extract potential information within the pseudo-labels with different confidence levels. Through experimental validation on the pomegranate growth stage dataset, the results show that the proposed method achieves an average accuracy of 90.8% with only 10% labeled data, which is 5.5 percentage points higher than the YOLOv8 network under the same dataset, and this performance is close to the 92% of the YOLOv8 trained with fully labeled data. This result fully demonstrates that the method proposed in this study can still train efficient and accurate object detection models in the context of limited labeling resources.

Key words: semi-supervised, pseudo-label, object detection, teacher-student model

张立才, 张欣, 陈孝玉龙, 刘佳明, 文兴甜, 杨胜贤. 改进伪标签半监督方法的石榴生长阶段检测模型[J]. 计算机工程与应用, 2025, 61(13): 291-299.

ZHANG Licai, ZHANG Xin, CHEN Xiaoyulong, LIU Jiaming, WEN Xingtian, YANG Shengxian. Improved Pseudo-Labeled Semi-Supervised Method for Pomegranate Growth Stage Detection Modeling[J]. Computer Engineering and Applications, 2025, 61(13): 291-299.

参考文献

[1] 张阳婷, 黄德启, 王东伟, 等. 基于深度学习的目标检测算法研究与应用综述[J]. 计算机工程与应用, 2023, 59(18): 1-13.
ZHANG Y T, HUANG D Q, WANG D W, et al. Review on research and application of deep learning-based target detection algorithms[J]. Computer Engineering and Applications, 2023, 59(18): 1-13.
[2] ZHENG Q H, SAPONARA S, TIAN X Y, et al. A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT[J]. Cognitive Neurodynamics, 2024, 18(2): 659-671.
[3] ZHENG Q H, TIAN X Y, YU Z G, et al. MobileRaT: a lightweight radio transformer method for automatic modulation classification in drone communication systems[J]. Drones, 2023, 7(10): 596.
[4] 金寿祥, 周宏平, 姜洪喆, 等. 采摘机器人视觉系统研究进展[J]. 江苏农业学报, 2023, 39(2): 582-595.
JIN S X, ZHOU H P, JIANG H Z, et al. Research progress on visual system of picking robot[J]. Jiangsu Journal of Agricultural Sciences, 2023, 39(2): 582-595.
[5] 周飞, 郭杜杜, 王洋, 等. 基于改进YOLOv8的交通监控车辆检测算法[J]. 计算机工程与应用, 2024, 60(6): 110-120.
ZHOU F, GUO D D, WANG Y, et al. Vehicle detection algorithm based on improved YOLOv8 in traffic surveillance[J]. Computer Engineering and Applications, 2024, 60(6): 110-120.
[6] 张剑锐, 魏霞, 张林鍹, 等. 改进YOLO v7的绝缘子检测与定位[J]. 计算机工程与应用, 2024, 60(4): 183-191.
ZHANG J R, WEI X, ZHANG L X, et al. Improving detection and positioning of insulators in YOLO v7[J]. Computer Engineering and Applications, 2024, 60(4): 183-191.
[7] 李文强, 陈莉, 谢旭, 等. 改进YOLOv5的X光图像违禁品检测算法[J]. 计算机工程与应用, 2023, 59(16): 170-176.
LI W Q, CHEN L, XIE X, et al. Algorithm for detecting prohibited items in X-ray images based on improved YOLOv5[J]. Computer Engineering and Applications, 2023, 59(16): 170-176.
[8] 邵延华, 张铎, 楚红雨, 等. 基于深度学习的YOLO目标检测综述[J]. 电子与信息学报, 2022, 44(10): 3697-3708.
SHAO Y H, ZHANG D, CHU H Y, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3697-3708.
[9] TARVAINEN A, VALPOLA H. Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results[C]//Advances in Neural Information Processing Systems, 2017.
[10] DENG J H, LI W, CHEN Y H, et al. Unbiased mean teacher for cross-domain object detection[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 4091-4101.
[11] LIU Y Y, TIAN Y, CHEN Y H, et al. Perturbed and strict mean teachers for semi-supervised semantic segmentation[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 4258-4267.
[12] LIU Y C, MA C Y, HE Z J, et al. Unbiased teacher for semi-supervised object detection[J]. arXiv:2102.09480, 2021.
[13] 李庆耀. 基于半监督学习的玉米雄穗计数和抽穗状态识别方法研究[D]. 泰安: 山东农业大学, 2023.
LI Q Y. Research on the method of maize tassel counting and heading state recognition based on semi-supervised learning[D]. Tai’an: Shandong Agricultural University, 2023.
[14] 吕佳, 李帅军, 曾梦瑶, 等. 基于半监督SPM-YOLOv5的套袋柑橘检测算法[J]. 农业工程学报, 2022, 38(18): 204-211.
LYU J, LI S J, ZENG M Y, et al. Detecting bagged citrus using a semi-supervised SPM-YOLOv5[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(18): 204-211.
[15] LIN S D, LI J Y, HUANG D Y, et al. Early detection of rice blast using a semi-supervised contrastive unpaired translation iterative network based on UAV images[J]. Plants, 2023, 12(21): 3675.
[16] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[J]. arXiv:1503.02531, 2015.
[17] SOHN K, BERTHELOT D, LI C L, et al. FixMatch: simplifying semi-supervised learning with consistency and confidence[C]//Advances in Neural Information Processing Systems, 2020: 596-608.
[18] PELáEZ-VEGAS A, MESEJO P, LUENGO J. A survey on semi-supervised semantic segmentation[J]. arXiv:2302.09899, 2023.
[19] BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS: improving object detection with one line of code[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 5561-5569.
[20] CHEN B B, CHEN W J, YANG S C, et al. Label matching semi-supervised object detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 14361-14370.
[21] ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12993-13000.
[22] ZHAO J F, ALMODFER R, WU X Y, et al. A dataset of pomegranate growth stages for machine learning-based monitoring and analysis[J]. Data in Brief, 2023, 50: 109468.
[23] LIU Y C, MA C Y, KIRA Z. Unbiased teacher v2: semi-supervised object detection for anchor-free and anchor-based detectors[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 9809-9818.
[24] CHEN B H, LI P Y, CHEN X, et al. Dense learning based semi-supervised object detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 4805-4814.
[25] XU M D, ZHANG Z, HU H, et al. End-to-end semi-supervised object detection with soft teacher[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 3040-3049.
[26] TIAN Z, SHEN C H, CHEN H, et al. FCOS: a simple and strong anchor-free object detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4): 1922-1933.
[27] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[28] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 2020, 128(2): 336-359.