Abstract
Object detection models have made significant progress and achieved state-of-the-art performance, which can now be comparable to human experts in various domains. However, training such models often requires a large amount of labeled data, which can be challenging to obtain. To address this issue, Active Learning (AL) has emerged as a technique to enhance the efficiency of deep learning models by reducing the amount of data and time required to train models to a satisfactory level.
In this paper, in the context of smart farming, we propose to study the impact of AL in object detection models trained with a small dataset of labelled images of whitefly-infested tomato leaves. We use YOLOv5 and fit the bounding box with confidence as a score function to select the most active relevant examples. The results show a trade-off between performance and cost suggesting AL outweigh the associated costs when labelled training data is limited.
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Notes
- 1.
Open-source online tool: https://github.com/heartexlabs/labelImg.
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Acknowledgments
This work was supported by project PEGADA 4.0 (PRR-C05-i03-000099), financed by the PPR - Plano de Recuperação e Resiliência and by national funds through FCT, within the scope of the project CISUC (UID/CEC/00326/2020).
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Costa, D., Silva, C., Costa, J., Ribeiro, B. (2023). Optimizing Object Detection Models via Active Learning. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_7
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DOI: https://doi.org/10.1007/978-3-031-36616-1_7
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