- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Faculty Research and Publications /
- Detection and Counting of Small Target Apples under...
Open Collections
UBC Faculty Research and Publications
Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny Ma, Li; Zhao, Liya; Wang, Zixuan; Zhang, Jian (Professor of biology); Chen, Guifen
Abstract
Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation.
Item Metadata
| Title |
Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny
|
| Creator | |
| Publisher |
Multidisciplinary Digital Publishing Institute
|
| Date Issued |
2023-05-20
|
| Description |
Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation.
|
| Subject | |
| Genre | |
| Type | |
| Language |
eng
|
| Date Available |
2023-09-07
|
| Provider |
Vancouver : University of British Columbia Library
|
| Rights |
CC BY 4.0
|
| DOI |
10.14288/1.0435768
|
| URI | |
| Affiliation | |
| Citation |
Agronomy 13 (5): 1419 (2023)
|
| Publisher DOI |
10.3390/agronomy13051419
|
| Peer Review Status |
Reviewed
|
| Scholarly Level |
Faculty; Researcher
|
| Rights URI | |
| Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
CC BY 4.0