基于半监督伪标签学习的小麦头检测算法
1. 西部州长大学, 信息科学学院, 美国盐湖城 84107
2. 南昌市第九医院, 传染科, 江西南昌 330046
| 摘 要： | 小麦是全球性的粮食作物，利用基于深度学习的小麦头检测算法有利于简化种植流程，降低种植成本，提高小麦产量。但是由于全球小麦的性状多样、生长周期不一致，构建一个能够在各种场景都保持高鲁棒性以及高精确度的检测模型至关重要。对此，提出了基于深度学习的小麦头检测算法，对于缺少丰富领域数据的小麦头检测场景，提出利用半监督伪标签生成学习算法对无标注的数据进行半监督标注并用于模型训练，提高在新特征域上的泛化性能。此外，为了平衡小麦头检测算法的速度与精度，设计了多模型融合机制，对具有不同特性的检测算法进行融合。最后，介绍了公开小麦头检测数据集，并将其用于算法验证，提出的算法在其验证集上获得95.2%的平均准确率，在测试集上获得76.1%的平均准确率，表明提出的算法具备对跨域场景的鲁棒能力以及优异的检测性能。 |
| 关 键 词： | 目标检测; 半监督学习; 伪标签学习; 农业; 深度学习; 神经网络 |
| DOI： | 10.57237/j.cst.2023.04.006 |
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收稿日期: 2023-10-17
接受日期: 2023-11-15
在线出版日期: 2023-11-27
Semi-supervised Pseudo Label Learning-based Wheat Head Detection
1. Department of Information Technology, Western Governors University, Salt Lake City 84107, USA
2. Department of Infection Diseases, Nanchang No. 9 Hospital, Nanchang 330046, China
| Abstract: | Wheat is a global food crop. Deep learning-based wheat head detection algorithm is helpful to simplify the planting process, reduce the planting cost and improve the wheat yield. However, due to the diversity of wheat traits and in-consistent growth cycles around the world, it is very important to build a detection model that can maintain high robustness and high accuracy in various scenarios. A deep learning-based wheat head detection algorithm was proposed. For the situation that domain data was scarce for wheat head detection, a semi-supervised pseudo label generation algorithm was proposed to semi supervised label the unlabeled data and use it for model training to improve the generalization performance in the new feature domain. In addition, in order to balance the speed and accuracy of wheat head detection algorithm, a multi model fusion mechanism was designed to fuse the detection algorithms with different characteristics. Finally, the open-source wheat head detection dataset was introduced and was used for algorithm verification. The algorithm obtained an average accuracy of 95.2% on its validation set and 76.1% on the test set, which showed that the proposed algorithm had robustness to cross domain scenes and excellent detection performance. |
| Keywords: | Object Detection; Semi-supervised Learning; Pseudo Label Learning; Agriculture; Deep Learning; Neural Network |
参考文献
| [1] | REYNOLDS M P, BORLAUG N E. Applying innovations and new technologies for international collaborative wheat improvement [J]. The Journal of Agricultural Science, 2006, 144(2): 95-110. |
| [2] | Youwei, L. (2023). Real-time eye blink detection using general cameras: a facial landmarks approach. International Science Journal of Engineering & Agriculture, 2(5), 1-8. |
| [3] | Lu, Y. (2023, July). REAL-TIME EYE BLINK DETECTION WITH GENERAL CAMERAS. In The 28th International scientific and practical conference “Science and development of methods for solving modern problems (pp. 198-201). |
| [4] | 陈妮亚, 阮佳阳, 黄金苗, 等. 结合深度学习与生物特征识别在冷链拣选中的算法研究[J]. 智能科学与技术学报, 2019, 1(1): 88-95. |
| [5] | CHEN N Y, RUAN J Y, HUANG J M, et al. Algorithm design for food-picking combining deep learning and biometrics recognition [J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(1): 88-95. |
| [6] | Liu, Yiming, and Yi Bao. "Review on automated condition assessment of pipelines with machine learning." Advanced Engineering Informatics 53(2022): 101687. |
| [7] | Liu, Yiming, and Yi Bao. "Review of robot-based damage assessment for offshore with turbines." Renewable and Sustainable Energy Reviews 158 (2022): 112187. |
| [8] | Liu, Yiming, and Yi Bao. "Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning." Measurement 220(2023): 113418. |
| [9] | Zeng, Lu, et al. "Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis." Information Processing & Management 59.4(2022): 102952. |
| [10] | Xiao, Tingsong, et al. "Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022. |
| [11] | Wang, Xiaochen, et al. "MRMRP: multi-source review-based model for rating prediction." Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part II 25. Springer International Publishing, 2020. |
| [12] | Wang, Xiaochen, Tingsong Xiao, and Jie Shao. "EMRM: Enhanced Multi-source Review-Based Model for Rating Prediction." Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part III 14. Springer International Publishing, 2021. |
| [13] | Hu, Jing, et al. "M-GCN: Multi-scale Graph Convolutional Network for 3D Point Cloud Classification." 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2023. |
| [14] | Qiu, Yujuan. ESTIMATION OF TAIL RISK MEASURES IN FINANCE: APPROACHES TO EXTREME VALUE MIXTURE MODELING. Diss. Johns Hopkins University, 2019. |
| [15] | Dai, W., Mou, C., Wu, J., & Ye, X. (2023, May). Diabetic Retinopathy Detection with Enhanced Vision Transformers: The Twins-PCPVT Solution. In 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 403-407). IEEE. |
| [16] | Mou, C., Ye, X., Wu, J., & Dai, W. (2023, April). Automated ICD Coding Based on Neural Machine Translation. In 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 495-500). IEEE. |
| [17] | Ye, X., Wu, J., Mou, C., & Dai, W. (2023, May). MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression. In 2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) (pp. 169-175). IEEE. |
| [18] | Mou, C., Dai, W., Ye, X., & Wu, J. (2023, July). Research On Method Of User Preference Analysis Based on Entity Similarity and Semantic Assessment. In 2023 8th International Conference on Signal and Image Processing (ICSIP) (pp. 1029-1033). IEEE. |
| [19] | HAMUDA E, MC GINLEY B, GLAVIN M, et al. Improved image processing-base crop detection using Kalman filtering and the Hungarian algorithm [J]. Computers and Electronics in Agriculture, 2018, 148: 37-44. |
| [20] | LU J, SANG N. Detecting citrus fruits and occlusion recovery under natural illumination conditions [J]. Computers and Electronics in Agriculture, 2015, 110: 121-130. [LinkOut] |
| [21] | LINKER R, COHEN O, NAOR A. Determination of the number of green apples in RGB images recorded in orchards [J]. Computers and Electronics in Agriculture, 2012, 81: 45-57. |
| [22] | KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90. |
| [23] | Zhang, Z., & Hu, R. (2023). Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy. arXiv preprint arXiv: 2309.03437. |
| [24] | LEE S H, CHAN C S, MAYO S J, et al. How deep learning extracts and learns leaf features for plant classification [J]. Pattern Recognition, 2017, 71: 1-13. |
| [25] | Wu, J., Ye, X., Mou, C., & Dai, W. (2023, May). Fineehr: Refine clinical note representations to improve mortality prediction. In 2023 11th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE. |
| [26] | RAHNEMOONFAR M, SHEPPARD C. Deep count: fruit counting based on deep simulated learning [J]. Sensors, 2017, 17(4): 905. |
| [27] | ZHANG Y D, DONG Z C, CHEN X Q, et al. Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation [J]. Multimedia Tools and Applications, 2019, 78(3): 3613-3632. |
| [28] | CHEN S W, SHIVAKUMAR S S, DCUNHA S, et al. Counting apples and oranges with deep learning: a data-driven approach [J]. IEEE Robotics and Automation Letters, 2017, 2(2): 781-788. |
| [29] | DYRMANN M, JØRGENSEN R N, MIDTIBY H S. RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network [J]. Advances in Animal Biosciences, 2017, 8(2): 842-847. |
| [30] | Cheng, S., Chu, B., Zhong, B., Zhang, Z., Liu, X., Tang, Z., & Li, X. (2021). DRNet: Towards fast, accurate and practical dish recognition. Science China Technological Sciences, 64(12), 2651-2661. |
| [31] | GIRSHICK R. Fast R-CNN [C] // Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1440-1448. |
| [32] | REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 779-788. |
| [33] | BARGOTI S, UNDERWOOD J. Deep fruit detection in orchards [C] // Proceedings of the 2017 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2017: 3626-3633. |
| [34] | LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector [M] // Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 21-37. |
| [35] | LAW H, DENG J. CornerNet: detecting objects as paired keypoints [J]. International Journal of Computer Vision, 2020, 128(3): 642-656. |
| [36] | CAI Z W, FAN Q F, FERIS R S, et al. A unified multi-scale deep convolutional neural network for fast object detection [M] // Computer Vision - ECCV 2016. Cham: Springer International Publishing. |
| [37] | LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 936-944. |
| [38] | LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation [C] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 8759-8768. |
| [39] | Zhang, Z., Zhong, B., Zhang, S., Tang, Z., Liu, X., & Zhang, Z. (2021). Distractor-aware fast tracking via dynamic convolutions and mot philosophy. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1024-1033). |
| [40] | Liang, Z., Cao, H., Yang, C., Zhang, Z., & Chen, G. (2022, September). Global-local feature aggregation for event-based object detection on eventkitti. In 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 1-7). IEEE. |
| [41] | GHIASI G, LIN T Y, LE Q V. NAS-FPN: learning scalable feature pyramid architecture for object detection [C] // Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 7029-7038. |
| [42] | TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection [C] // Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 10778-10787. |
| [43] | CHAPELLE O, SCHOLKOPF B, ZIEN E. Semi-supervised learning [J]. IEEE Transactions on Neural Networks, 2009, 20(3): 542. |
| [44] | CINBIS R G, VERBEEK J, SCHMID C. Weakly supervised object localization with multi-fold multiple instance learning [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(1): 189-203. |
| [45] | HOFFMAN J, GUADARRAMA S, TZENG E, et al. LSDA: large scale detection through adaptation [M] // Advances in Neural Information Processing Systems. [S.l.:s.n.], 2014: 3536–3544. |
| [46] | TAN M X, LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks [C] // Proceedings of the International Conference on Machine Learning. PMLR, 2019: 6105-6114. |
| [47] | CHAPELLE O, ZIEN A. Semi-supervised classification by low density separation [C] // Proceedings of the International workshop on artificial intelligence and statistics. [S.l.:s.n.], 2005: 57-64. |
| [48] | DAVID E, MADEC S, SADEGHI-TEHRAN P, et al. Global wheat head detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods [J]. Plant Phenomics, 2020, 2020: 1-12. |
| [49] | REDMON J, FARHADI A. Yolov3: an incremental improvement [J]. arXiv preprint, 2018, arXiv: 1804.02767. |
| [50] | BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection [J]. arXiv preprint, 2020, arXiv: 2004.10934. |
