YOLOv5-tassel: Detecting Tassels in RGB UAV Imagery with Improved YOLOv5 Based on Transfer Learning
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 an...
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| Vydáno v: | IEEE journal of selected topics in applied earth observations and remote sensing Ročník 15; s. 1 - 10 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 1939-1404, 2151-1535 |
| On-line přístup: | Získat plný text |
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| Abstract | 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 bi-directional feature pyramid network (BiFPN) 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 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 <inline-formula><tex-math notation="LaTeX">mAP</tex-math></inline-formula> value of 44.7%, which is better than well-known object detection approaches such as FCOS, RetinaNet, and YOLOv5. |
|---|---|
| AbstractList | 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. 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 bi-directional feature pyramid network (BiFPN) 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 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 <inline-formula><tex-math notation="LaTeX">mAP</tex-math></inline-formula> value of 44.7%, which is better than well-known object detection approaches such as FCOS, RetinaNet, and YOLOv5. |
| Author | Liu, Wei Crawford, Melba Quijano, Karoll |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0003-2468-8842 surname: Liu fullname: Liu, Wei organization: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA – sequence: 2 givenname: Karoll surname: Quijano fullname: Quijano, Karoll organization: Department of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN, USA – sequence: 3 givenname: Melba orcidid: 0000-0003-3459-2094 surname: Crawford fullname: Crawford, Melba organization: School of Civil Engineering and School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA |
| BackLink | https://www.osti.gov/biblio/1889504$$D View this record in Osti.gov |
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| Snippet | Unmanned Aerial Vehicles (UAVs) equipped with lightweight sensors such as RGB cameras and LiDAR have significant potential in precision agriculture, including... Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture,... |
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| SubjectTerms | Aggregation Agriculture Algorithms Annotations Cameras CenterNet Deep learning Dependent variables Detection Feature extraction Head Image acquisition Imagery Learning Lidar Neck Object detection Object recognition Precision agriculture SimAM attention module Small tassel detection Transfer learning Unmanned aerial vehicles YOLOv5 |
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| Title | YOLOv5-tassel: Detecting Tassels in RGB UAV Imagery with Improved YOLOv5 Based on Transfer Learning |
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