Claim · model-written summary
YOLO-CSR improved mAP@0.5 by 1.18% compared with YOLOv5.
- Taxon
- red clover
Trifolium pratenset0 - Outcome
- mAP@0.5e5
- Direction
- improvemente5
- Value
- 1.18%e5
- Comparator
- YOLOv5e5
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“coated red clover seeds”
e5“Compared to YOLOv5, YOLO-CSR showed a 1.18% improvement in mAP@0.5”
Title
Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module
Xiwen Zhang; Chuanzhong Xuan; Zhanfeng Hou International journal of agricultural and biological engineering 2023. doi:10.25165/j.ijabe.20231606.7773
Other claims from this paper (8)
- Method / resource YOLO-CSR achieved the best recognition performance on the self-built coated seed image dataset compared with other classical recognition models.
- Method / resource Average precision for one of the three coated seed types reached 98.43%.
- Method / resource Average precision for one of the three coated seed types reached 97.91%.
- Method / resource Average precision for one of the three coated seed types reached 97.26%.
- Method / resource Mean average precision at 0.5 reached 97.87%.
- Method / resource YOLO-CSR has a model size of 14.9 MB.
- Method / resource YOLO-CSR has an average recognition time of 10.1 ms.
- Method / resource YOLO-CSR runs at a frame per second of 99.