Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 100-114.doi: 10.12133/j.smartag.2021.3.2.202105-SA005
• Information Processing and Decision Making • Previous Articles Next Articles
LI Zhijun1,2(), YANG Shenghui1,2, SHI Deshuai1,2, LIU Xingxing1,2, ZHENG Yongjun1,2(
)
Received:
2021-05-13
Revised:
2021-06-10
Online:
2021-06-30
Published:
2021-08-25
corresponding author:
Yongjun ZHENG
E-mail:335022969@qq.com;zyj@cau.edu.cn
CLC Number:
LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun. Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5[J]. Smart Agriculture, 2021, 3(2): 100-114.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2021.3.2.202105-SA005
Table 1
Sample data-sets and data volume
着色天数/d | 训练集 | 测试集1 | 测试集2 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
顺光子集 | 侧光子集 | 逆光子集 | ||||||||
图片数量/张 | 目标框数量/个 | 图片数量/张 | 顺光目标框数量/个 | 图片数量/张 | 侧光目标框数量/个 | 图片数量/张 | 逆光目标框数量/个 | 图片数量/张 | 目标框数量/个 | |
1 | 600 | 15,017 | 100 | 3282 | 100 | 3027 | 100 | 2963 | 100 | 3125 |
8 | 600 | 16,639 | 100 | 3155 | 100 | 3241 | 100 | 2834 | 100 | 3272 |
15 | 600 | 15,892 | 100 | 3268 | 100 | 3114 | 100 | 3052 | 100 | 3136 |
总计 | 1800 | 47,548 | 300 | 9705 | 300 | 9382 | 300 | 8849 | 300 | 9533 |
Table 2
Comparison experiment results of the detection speed of the algorithm before and after the improvement
算法 | 检测速度及相对提升率 | YOLOv5s | YOLOv5m | YOLOv5l | YOLOv5x | |
---|---|---|---|---|---|---|
改进前 | 检测速度/ms | 37.16 0.00 | 84.87 0.00 | 152.33 0.00 | 310.53 0.00 | |
相对提升率/% | ||||||
改进后 | 单独更换深度可分离卷积 | 检测速度/ms | 30.73 -17.28 | 70.85 -16.51 | 132.26 13.17 | 275.54 -11.26 |
相对提升率/% | ||||||
单独嵌入注意力机制模块 | 检测速度/ms | 37.83 1.80 | 87.24 2.79 | 155.87 2.32 | 318.66 2.61 | |
相对提升率/% | ||||||
融合深度可分离卷积和注意力机制模块 | 检测速度/ms | 31.44 -15.37 | 72.90 -14.10 | 136.28 -10.50 | 281.49 -9.35 | |
相对提升率/% |
Table 3
Comparison of experiment results of the original algorithm and the improved algorithm of the mAP
算法 | 平均准确率及绝对提升率 | YOLOv5s | YOLOv5m | YOLOv5l | YOLOv5x | |
---|---|---|---|---|---|---|
改进前 | 平均准确率 | 89.83 0.00 | 90.75 0.00 | 92.07 0.00 | 93.44 0.00 | |
绝对提升率 | ||||||
改进后 | 单独更换深度可分离卷积 | 平均准确率 | 88.94 -0.89 | 90.05 -0.70 | 91.55 -0.52 | 92.77 -0.67 |
绝对提升率 | ||||||
单独嵌入注意力机制模块 | 平均准确率 | 92.10 2.27 | 94.62 3.87 | 95.63 3.56 | 96.45 3.01 | |
绝对提升率 | ||||||
融合深度可分离卷积和注意力机制模块 | 平均准确率 | 92.88 3.05 | 93.99 3.24 | 95.15 3.08 | 96.27 2.83 | |
绝对提升率 |
Table 4
MAP comparison results of different data sets
模型 | 着色1 d数据集 | 着色8 d数据集 | 着色15 d数据集 | ||||||
---|---|---|---|---|---|---|---|---|---|
顺光 | 侧光 | 逆光 | 顺光 | 侧光 | 逆光 | 顺光 | 侧光 | 逆光 | |
改进型YOLOv5 | 90.25 | 92.48 | 89.31 | 93.56 | 95.08 | 92.95 | 95.26 | 96.79 | 94.07 |
YOLOv5 | 89.56 | 90.57 | 87.35 | 91.78 | 92.23 | 90.57 | 93.57 | 94.29 | 92.38 |
YOLOv3 | 87.82 | 88.11 | 86.77 | 89.93 | 90.86 | 88.75 | 92.27 | 93.33 | 91.43 |
SSD | 86.26 | 86.98 | 83.67 | 87.69 | 89.54 | 88.37 | 90.25 | 91.33 | 89.40 |
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