The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning
Open accessAbstract
Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture, including object detection. Tassel detection in maize is an essential trait given its relevance as the beginning of the reproductive stage of growth and development of the plants. However, compared with general object detection, tassel detection based on RGB imagery acquired by UAVs is more challenging due to the small size, time-dependent variable shape, and complexity of the objects of interest. A novel algorithm referred to as YOLOv5-tassel is proposed to detect tassels in UAV-based RGB imagery. A bidirectional feature pyramid network is adopted for the path-aggregation neck to effectively fuse cross-scale features. The robust attention module of SimAM is introduced to extract the features of interest before each detection head. An additional detection head is also introduced to improve small-size tassel detection based on the original YOLOv5. Annotation is performed with guidance from center points derived from CenterNet to improve the selection of the bounding boxes for tassels. Finally, to address the issue of limited reference data, transfer learning based on the VisDrone dataset is adopted. Testing results for our proposed YOLOv5-tassel method achieved the mAP value of 44.7%, which is better than well-known object detection approaches, such as FCOS, RetinaNet, and YOLOv5.
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., Jindal, A., & Singh, A. K. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,040
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Cited by
Realtime Vehicle Tracking Method Based on YOLOv5 + DeepSORT
Lin, L., He, H., Xu, Z., & Wu, D. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/7974201 · Cited by 18
“[33] proposed a novel algorithm referred to as YOLOv5-tassel to detect tassels.”
MethodYOLOdrive: A Lightweight Autonomous Driving Single-Stage Target Detection Approach
Wang, L., Hua, S., Zhang, C., Yang, G., Ren, J., & Li, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3439863 · Cited by 14
Metadata
- Authors
- Liu, W., Quijano, K., & Crawford, M. M.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2022
- Volume
- 15
- Pages
- 8085-8094
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W4295832403
- Cited by
- 270
- References
- 64
- FWCI
- 30.82
- Type
- journal article
- Language
- en
- Open access
- gold
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- Liu, W., Quijano, K., & Crawford, M. M. (2022). YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2022.3206399
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