CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device
Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a l...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/19/4647 |
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| author | Yali Zhang Xipeng Fang Jun Guo Linlin Wang Haoxin Tian Kangting Yan Yubin Lan |
| author_facet | Yali Zhang Xipeng Fang Jun Guo Linlin Wang Haoxin Tian Kangting Yan Yubin Lan |
| author_sort | Yali Zhang |
| collection | DOAJ |
| description | Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards. |
| first_indexed | 2024-03-10T21:37:13Z |
| format | Article |
| id | doaj.art-d29cdeeb8933446db4757bf7bd4c81f8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T21:37:13Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d29cdeeb8933446db4757bf7bd4c81f82023-11-19T14:58:02ZengMDPI AGRemote Sensing2072-42922023-09-011519464710.3390/rs15194647CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded DeviceYali Zhang0Xipeng Fang1Jun Guo2Linlin Wang3Haoxin Tian4Kangting Yan5Yubin Lan6College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, ChinaSchool of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, ChinaGuangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, ChinaData processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards.https://www.mdpi.com/2072-4292/15/19/4647citrus treesremote sensingYOLOv7lightweighttarget detector |
| spellingShingle | Yali Zhang Xipeng Fang Jun Guo Linlin Wang Haoxin Tian Kangting Yan Yubin Lan CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device Remote Sensing citrus trees remote sensing YOLOv7 lightweight target detector |
| title | CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device |
| title_full | CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device |
| title_fullStr | CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device |
| title_full_unstemmed | CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device |
| title_short | CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device |
| title_sort | curi yolov7 a lightweight yolov7tiny target detector for citrus trees from uav remote sensing imagery based on embedded device |
| topic | citrus trees remote sensing YOLOv7 lightweight target detector |
| url | https://www.mdpi.com/2072-4292/15/19/4647 |
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