Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits
Blueberries, as one of the more economically rewarding fruits in the fruit industry, play a significant role in fruit detection during their growing season, which is crucial for orchard farmers’ later harvesting and yield prediction. Due to the small size and dense growth of blueberry fruits, manual...
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MDPI AG
2024-10-01
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| Series: | Agriculture |
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| author | Yun Zhao Yang Li Xing Xu |
| author_facet | Yun Zhao Yang Li Xing Xu |
| author_sort | Yun Zhao |
| collection | DOAJ |
| description | Blueberries, as one of the more economically rewarding fruits in the fruit industry, play a significant role in fruit detection during their growing season, which is crucial for orchard farmers’ later harvesting and yield prediction. Due to the small size and dense growth of blueberry fruits, manual detection is both time-consuming and labor-intensive. We found that there are few studies utilizing drones for blueberry fruit detection. By employing UAV remote sensing technology and deep learning techniques for detection, substantial human, material, and financial resources can be saved. Therefore, this study collected and constructed a UAV remote sensing target detection dataset for blueberry canopy fruits in a real blueberry orchard environment, which can be used for research on remote sensing target detection of blueberries. To improve the detection accuracy of blueberry fruits, we proposed the PAC3 module, which incorporates location information encoding during the feature extraction process, allowing it to focus on the location information of the targets and thereby reducing the chances of missing blueberry fruits. We adopted a fast convolutional structure instead of the traditional convolutional structure, reducing the model’s parameter count and computational complexity. We proposed the PF-YOLO model and conducted experimental comparisons with several excellent models, achieving improvements in mAP of 5.5%, 6.8%, 2.5%, 2.1%, 5.7%, 2.9%, 1.5%, and 3.4% compared to Yolov5s, Yolov5l, Yolov5s-p6, Yolov5l-p6, Tph-Yolov5, Yolov8n, Yolov8s, and Yolov9c, respectively. We also introduced a non-maximal suppression algorithm, Cluster-NMF, which accelerates inference speed through matrix parallel computation and merges multiple high-quality target detection frames to generate an optimal detection frame, enhancing the efficiency of blueberry canopy fruit detection without compromising inference speed. |
| format | Article |
| id | doaj-art-4f7eb6dd284348dd9afa62e9785d8b51 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-4f7eb6dd284348dd9afa62e9785d8b512025-08-20T02:11:11ZengMDPI AGAgriculture2077-04722024-10-011410184210.3390/agriculture14101842Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy FruitsYun Zhao0Yang Li1Xing Xu2School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaBlueberries, as one of the more economically rewarding fruits in the fruit industry, play a significant role in fruit detection during their growing season, which is crucial for orchard farmers’ later harvesting and yield prediction. Due to the small size and dense growth of blueberry fruits, manual detection is both time-consuming and labor-intensive. We found that there are few studies utilizing drones for blueberry fruit detection. By employing UAV remote sensing technology and deep learning techniques for detection, substantial human, material, and financial resources can be saved. Therefore, this study collected and constructed a UAV remote sensing target detection dataset for blueberry canopy fruits in a real blueberry orchard environment, which can be used for research on remote sensing target detection of blueberries. To improve the detection accuracy of blueberry fruits, we proposed the PAC3 module, which incorporates location information encoding during the feature extraction process, allowing it to focus on the location information of the targets and thereby reducing the chances of missing blueberry fruits. We adopted a fast convolutional structure instead of the traditional convolutional structure, reducing the model’s parameter count and computational complexity. We proposed the PF-YOLO model and conducted experimental comparisons with several excellent models, achieving improvements in mAP of 5.5%, 6.8%, 2.5%, 2.1%, 5.7%, 2.9%, 1.5%, and 3.4% compared to Yolov5s, Yolov5l, Yolov5s-p6, Yolov5l-p6, Tph-Yolov5, Yolov8n, Yolov8s, and Yolov9c, respectively. We also introduced a non-maximal suppression algorithm, Cluster-NMF, which accelerates inference speed through matrix parallel computation and merges multiple high-quality target detection frames to generate an optimal detection frame, enhancing the efficiency of blueberry canopy fruit detection without compromising inference speed.https://www.mdpi.com/2077-0472/14/10/1842target detectiondrone imageshigh-resolution imagesPF-YOLOcluster-NMF |
| spellingShingle | Yun Zhao Yang Li Xing Xu Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits Agriculture target detection drone images high-resolution images PF-YOLO cluster-NMF |
| title | Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits |
| title_full | Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits |
| title_fullStr | Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits |
| title_full_unstemmed | Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits |
| title_short | Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits |
| title_sort | object detection in high resolution uav aerial remote sensing images of blueberry canopy fruits |
| topic | target detection drone images high-resolution images PF-YOLO cluster-NMF |
| url | https://www.mdpi.com/2077-0472/14/10/1842 |
| work_keys_str_mv | AT yunzhao objectdetectioninhighresolutionuavaerialremotesensingimagesofblueberrycanopyfruits AT yangli objectdetectioninhighresolutionuavaerialremotesensingimagesofblueberrycanopyfruits AT xingxu objectdetectioninhighresolutionuavaerialremotesensingimagesofblueberrycanopyfruits |