中国农业科技导报 ›› 2025, Vol. 27 ›› Issue (10): 118-133.DOI: 10.13304/j.nykjdb.2025.0044
• 智慧农业 农机装备 • 上一篇
收稿日期:
2025-01-20
接受日期:
2025-07-10
出版日期:
2025-10-15
发布日期:
2025-10-15
通讯作者:
崔艳荣
作者简介:
黄志豪 E-mail:2023710688@yangtzeu.edu.cn;
基金资助:
Zhihao HUANG(), Chengfang LU, Yanrong CUI(
), Ronghua HU
Received:
2025-01-20
Accepted:
2025-07-10
Online:
2025-10-15
Published:
2025-10-15
Contact:
Yanrong CUI
摘要:
针对果园环境下苹果果实目标重叠、光照不均且尺度不一等复杂场景特点,同时为满足模型落地部署时检测精度与计算资源之间的平衡需求,提出一种基于YOLO11n改进的轻量化苹果果实检测模型YOLO-AP。首先,结合幽灵卷积与动态卷积改进特征提取模块,提出一种GD_C3K2模块,增强模型特征提取能力的同时降低模型复杂度;设计一种全局-局部双流特征融合网络,通过简化双向特征金字塔网络与自适应下采样模块对颈部网络进行重构,并在网络首部上引入全局到局部空间聚合模块进一步增强模型全局和局部空间建模能力;结合坐标注意力机制和重参数化卷积构建轻量化共享卷积头RepCoord-LDH,减少模型复杂度的同时维持高检测精度;最后,引入WiseIOUv3作为模型边界框损失函数,优化边界框回归性能。结果表明,YOLO-AP的精确率、召回率和平均精确率分别达到89.1%、88.9%和96.1%,相较于基线模型YOLO11n分别提升0.5、0.1和1.0百分点,同时模型的参数量和浮点运算量分别为1.4 M和3.6 G,仅为基线模型的53.8%和54.5%。与主流检测算法对比,YOLO-AP在模型检测性能和复杂度等指标中也优于其他算法。综上所述,YOLO-AP模型可为复杂环境下的苹果检测提供有效的技术支持。
中图分类号:
黄志豪, 卢承方, 崔艳荣, 胡蓉华. YOLO-AP:基于改进YOLO11n的轻量级苹果果实检测算法[J]. 中国农业科技导报, 2025, 27(10): 118-133.
Zhihao HUANG, Chengfang LU, Yanrong CUI, Ronghua HU. YOLO-AP: a Lightweight Apple Fruit Detection Algorithm Based on Improved YOLO11n[J]. Journal of Agricultural Science and Technology, 2025, 27(10): 118-133.
图5 GL-DSFN结构注:蓝色块表示输入特征图,绿色块和黄色块分别表示经过上采样和ADown下采样后的中间结果与最终输出结果。
Fig. 5 GL-DSFN structureNote:The blue block represents the input feature map, while the green and yellow blocks denote the intermediate results and final output results after upsampling and ADown downsampling, respectively.
参数Parameter | 数值Value | 参数Parameter | 数值Value |
---|---|---|---|
随机种子Random seed | 0 | 训练轮数Epoch | 300 |
输入图像大小Input size | 640×640 | 训练批次Batchsize | 32 |
初始学习率Orignal learn rate | 10-2 | 动量因子Momentum factor | 0.937 |
优化器Optimizer | SGD | 早停次数Early stop | 100 |
表1 训练超参数设置
Table 1 Training hyperparameter settings
参数Parameter | 数值Value | 参数Parameter | 数值Value |
---|---|---|---|
随机种子Random seed | 0 | 训练轮数Epoch | 300 |
输入图像大小Input size | 640×640 | 训练批次Batchsize | 32 |
初始学习率Orignal learn rate | 10-2 | 动量因子Momentum factor | 0.937 |
优化器Optimizer | SGD | 早停次数Early stop | 100 |
编号 Number | C3K2模块 C3K2 module | 颈部网络 Neck network | 检测头 Detect head | 损失函数 Loss function | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|---|---|---|---|---|---|
GD-C3K2 | GL-DSFN | RepCoord-LDH | WiseIOUv3 | ||||||
1 | - | - | - | - | 2.6 | 6.6 | 88.6 | 89.0 | 95.1 |
2 | √ | - | - | - | 2.3 | 5.5 | 89.2 | 89.7 | 95.4 |
3 | √ | √ | - | - | 1.5 | 4.4 | 89.1 | 89.5 | 95.8 |
4 | √ | √ | √ | - | 1.4 | 3.6 | 88.9 | 88.8 | 95.7 |
5 | √ | √ | √ | √ | 1.4 | 3.6 | 89.1 | 88.9 | 96.1 |
表2 消融试验结果
Table 2 Results of ablation experiments
编号 Number | C3K2模块 C3K2 module | 颈部网络 Neck network | 检测头 Detect head | 损失函数 Loss function | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|---|---|---|---|---|---|
GD-C3K2 | GL-DSFN | RepCoord-LDH | WiseIOUv3 | ||||||
1 | - | - | - | - | 2.6 | 6.6 | 88.6 | 89.0 | 95.1 |
2 | √ | - | - | - | 2.3 | 5.5 | 89.2 | 89.7 | 95.4 |
3 | √ | √ | - | - | 1.5 | 4.4 | 89.1 | 89.5 | 95.8 |
4 | √ | √ | √ | - | 1.4 | 3.6 | 88.9 | 88.8 | 95.7 |
5 | √ | √ | √ | √ | 1.4 | 3.6 | 89.1 | 88.9 | 96.1 |
颈部网络 Neck network | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|---|---|
PANet(YOLO11n) | 2.3 | 5.5 | 89.2 | 89.7 | 95.4 |
BiFPN | 1.8 | 5.7 | 88.9 | 89.4 | 94.9 |
SlimNeck | 2.4 | 5.6 | 88.5 | 89.2 | 95.3 |
AFPN | 1.9 | 5.9 | 89.0 | 89.1 | 95.1 |
GL-DSFN | 1.5 | 4.4 | 89.1 | 89.5 | 95.8 |
表3 颈部网络不同特征融合方式对比试验结果
Table 3 Comparison of different feature fusion methods in neck network
颈部网络 Neck network | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|---|---|
PANet(YOLO11n) | 2.3 | 5.5 | 89.2 | 89.7 | 95.4 |
BiFPN | 1.8 | 5.7 | 88.9 | 89.4 | 94.9 |
SlimNeck | 2.4 | 5.6 | 88.5 | 89.2 | 95.3 |
AFPN | 1.9 | 5.9 | 89.0 | 89.1 | 95.1 |
GL-DSFN | 1.5 | 4.4 | 89.1 | 89.5 | 95.8 |
检测头 Detect head | 参数量 params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% |
---|---|---|---|---|---|
YOLO11-Head | 2.6 | 6.6 | 89.1 | 89.5 | 95.8 |
DyHead | 2.3 | 5.5 | 88.3 | 88.4 | 95.5 |
LM-YOLO | 1.4 | 3.7 | 88.1 | 87.2 | 95.1 |
RepCoord-LDH | 1.4 | 3.6 | 88.9 | 88.8 | 95.7 |
表4 轻量化检测头对比结果
Table 4 Comparison result of lightweight detection head
检测头 Detect head | 参数量 params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% |
---|---|---|---|---|---|
YOLO11-Head | 2.6 | 6.6 | 89.1 | 89.5 | 95.8 |
DyHead | 2.3 | 5.5 | 88.3 | 88.4 | 95.5 |
LM-YOLO | 1.4 | 3.7 | 88.1 | 87.2 | 95.1 |
RepCoord-LDH | 1.4 | 3.6 | 88.9 | 88.8 | 95.7 |
损失函数 Loss function | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|
CIoU(YOLO11n) | 88.8 | 88.6 | 95.7 |
DIoU | 87.7 | 88.1 | 95.3 |
GIoU | 88.8 | 85.5 | 95.2 |
WiseIoUv3 | 89.1 | 88.9 | 96.1 |
表5 损失函数对比结果
Table 5 Comparison result of loss functions
损失函数 Loss function | 精确率 Precision/% | 召回率 Recall /% | 平均精确率 mAP50/% |
---|---|---|---|
CIoU(YOLO11n) | 88.8 | 88.6 | 95.7 |
DIoU | 87.7 | 88.1 | 95.3 |
GIoU | 88.8 | 85.5 | 95.2 |
WiseIoUv3 | 89.1 | 88.9 | 96.1 |
模型名称 Model name | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% | 单批次帧 |
---|---|---|---|---|---|---|
Faster R-CNN | 60.1 | 246.3 | 82.1 | 86.2 | 88.7 | 39.5 |
SSD | 26.3 | 99.5 | 83.6 | 85.3 | 89.4 | 70.1 |
RT-DETR-R18 | 20.1 | 58.6 | 89.6 | 88.6 | 94.6 | 110.6 |
YOLOv5n | 1.9 | 4.5 | 88.2 | 87.9 | 94.5 | 193.8 |
YOLOv6n | 4.6 | 11.3 | 88.7 | 88.4 | 93.1 | 98.6 |
YOLOv7tiny | 5.7 | 13.0 | 89.0 | 87.2 | 92.4 | 167.5 |
YOLOv8n | 3.1 | 8.1 | 87.7 | 89.2 | 94.6 | 284.5 |
YOLOv9tiny | 2.2 | 7.8 | 88.3 | 88.4 | 94.1 | 116.5 |
YOLOv10n | 2.6 | 8.2 | 89.2 | 88.0 | 94.9 | 149.8 |
YOLO11n | 2.6 | 6.6 | 88.6 | 89.0 | 95.1 | 181.2 |
YOLO-AP | 1.4 | 3.6 | 89.1 | 88.9 | 96.1 | 189.6 |
表6 检测模型对比
Table 6 Comparison of detection models
模型名称 Model name | 参数量 Params/M | 浮点运算量 FLOPs/G | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% | 单批次帧 |
---|---|---|---|---|---|---|
Faster R-CNN | 60.1 | 246.3 | 82.1 | 86.2 | 88.7 | 39.5 |
SSD | 26.3 | 99.5 | 83.6 | 85.3 | 89.4 | 70.1 |
RT-DETR-R18 | 20.1 | 58.6 | 89.6 | 88.6 | 94.6 | 110.6 |
YOLOv5n | 1.9 | 4.5 | 88.2 | 87.9 | 94.5 | 193.8 |
YOLOv6n | 4.6 | 11.3 | 88.7 | 88.4 | 93.1 | 98.6 |
YOLOv7tiny | 5.7 | 13.0 | 89.0 | 87.2 | 92.4 | 167.5 |
YOLOv8n | 3.1 | 8.1 | 87.7 | 89.2 | 94.6 | 284.5 |
YOLOv9tiny | 2.2 | 7.8 | 88.3 | 88.4 | 94.1 | 116.5 |
YOLOv10n | 2.6 | 8.2 | 89.2 | 88.0 | 94.9 | 149.8 |
YOLO11n | 2.6 | 6.6 | 88.6 | 89.0 | 95.1 | 181.2 |
YOLO-AP | 1.4 | 3.6 | 89.1 | 88.9 | 96.1 | 189.6 |
模型 Model | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% |
---|---|---|---|
YOLO11n | 68.5 | 63.0 | 68.3 |
YOLO-AP | 69.0 | 66.1 | 69.2 |
表7 MinneApple数据集泛化性对比试验结果
Table 7 Generalization comparison test results of MinneApple datasetom
模型 Model | 精确率 Precision/% | 召回率 Recall/% | 平均精确率 mAP50/% |
---|---|---|---|
YOLO11n | 68.5 | 63.0 | 68.3 |
YOLO-AP | 69.0 | 66.1 | 69.2 |
图13 泛化数据集可视化结果比较注:MinneApple数据集对落地苹果不进行标签化。
Fig.13 Comparison of visualization results of generalized datasetsNote:MinneApple dataset does not label fallen apples.
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