Journal of Agricultural Science and Technology ›› 2022, Vol. 24 ›› Issue (9): 96-105.DOI: 10.13304/j.nykjdb.2021.0612
• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles Next Articles
Yue ZHAO1(), Yong WEI1, Huiyong SHAN1, Zhimin MU2, ZHANGJianxin1, Haiyun WU1, Hui ZHAO1(
), Jianlong HU3
Received:
2021-07-24
Accepted:
2021-11-22
Online:
2022-09-15
Published:
2022-10-11
Contact:
Hui ZHAO
赵越1(), 卫勇1, 单慧勇1, 穆志民2, 张健欣1, 吴海云1, 赵辉1(
), 胡建龙3
通讯作者:
赵辉
作者简介:
赵越 E-mail:119788584@qq.com;
基金资助:
CLC Number:
Yue ZHAO, Yong WEI, Huiyong SHAN, Zhimin MU, ZHANGJianxin, Haiyun WU, Hui ZHAO, Jianlong HU. Wheat Ear Detection Method Based on Deep Learning[J]. Journal of Agricultural Science and Technology, 2022, 24(9): 96-105.
赵越, 卫勇, 单慧勇, 穆志民, 张健欣, 吴海云, 赵辉, 胡建龙. 基于深度学习的高分辨率麦穗图像检测方法[J]. 中国农业科技导报, 2022, 24(9): 96-105.
模型 Model | 图片尺寸 Image size | 裁剪后分辨率 Cropped resolution | 图片数量 Image number |
---|---|---|---|
#1 | 6 000×4 000 | 500×500 | 3 000 |
#2 | 6 000×4 000 | 1 000×1 000 | 3 000 |
#3 | 6 000×4 000 | 1 500×1 500 | 3 000 |
#4 | 6 000×4 000 | 2 000×2 000 | 3 000 |
#5 | 6 000×4 000 | 2 500×2 500 | 3 000 |
#6 | 6 000×4 000 | 3 000×3 000 | 3 000 |
Table 1 Model with different resolution
模型 Model | 图片尺寸 Image size | 裁剪后分辨率 Cropped resolution | 图片数量 Image number |
---|---|---|---|
#1 | 6 000×4 000 | 500×500 | 3 000 |
#2 | 6 000×4 000 | 1 000×1 000 | 3 000 |
#3 | 6 000×4 000 | 1 500×1 500 | 3 000 |
#4 | 6 000×4 000 | 2 000×2 000 | 3 000 |
#5 | 6 000×4 000 | 2 500×2 500 | 3 000 |
#6 | 6 000×4 000 | 3 000×3 000 | 3 000 |
模型Model | 方法Method | 分辨率Resolution | 均类平均精度mAP/% |
---|---|---|---|
#1 | YOLOv4 | 500×500 | 90.6 |
#2 | YOLOv4 | 1 000×1 000 | 92.3 |
#3 | YOLOv4 | 1 500×1 500 | 93.7 |
#4 | YOLOv4 | 2 000×2 000 | 91.6 |
#5 | YOLOv4 | 2 500×2 500 | 90.3 |
#6 | YOLOv4 | 3 000×3 000 | 89.1 |
Table 3 Model performance at different resolutions
模型Model | 方法Method | 分辨率Resolution | 均类平均精度mAP/% |
---|---|---|---|
#1 | YOLOv4 | 500×500 | 90.6 |
#2 | YOLOv4 | 1 000×1 000 | 92.3 |
#3 | YOLOv4 | 1 500×1 500 | 93.7 |
#4 | YOLOv4 | 2 000×2 000 | 91.6 |
#5 | YOLOv4 | 2 500×2 500 | 90.3 |
#6 | YOLOv4 | 3 000×3 000 | 89.1 |
方法Method | 损失值Loss | F1值F1 score | 交并比IoU | 类平均精度mAP/% | 检测速度FPS |
---|---|---|---|---|---|
YOLOv3 | 2.4 | 0.786 | 0.842 | 89.4 | 57 |
YOLOv4-tiny | 2.8 | 0.764 | 0.823 | 87.3 | 137 |
YOLOv4 | 0.7 | 0.837 | 0.886 | 93.7 | 52 |
FasterR-CNN | 0.9 | 0.823 | 0.874 | 92.8 | 7 |
Table 4 Model performance of different network
方法Method | 损失值Loss | F1值F1 score | 交并比IoU | 类平均精度mAP/% | 检测速度FPS |
---|---|---|---|---|---|
YOLOv3 | 2.4 | 0.786 | 0.842 | 89.4 | 57 |
YOLOv4-tiny | 2.8 | 0.764 | 0.823 | 87.3 | 137 |
YOLOv4 | 0.7 | 0.837 | 0.886 | 93.7 | 52 |
FasterR-CNN | 0.9 | 0.823 | 0.874 | 92.8 | 7 |
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