轻量化改进的苹果园果实识别模型CS_YOLOv7

doi:10.3969/j.issn.1004-1524.20250100

浙江农业学报 ›› 2026, Vol. 38 ›› Issue (2): 383-396.DOI: 10.3969/j.issn.1004-1524.20250100

轻量化改进的苹果园果实识别模型CS_YOLOv7

欧阳宇(), 刘朔(), 李萌民, 张鹏

武汉轻工大学数学与计算机学院, 湖北武汉 430048

收稿日期:2025-02-10 出版日期:2026-02-25 发布日期:2026-03-24
作者简介:欧阳宇,研究方向为计算机视觉、目标检测。E-mail:528038794@qq.com
通讯作者: ^*刘朔,E-mail:874477154@qq.com
基金资助:
国家自然科学基金民航联合研究基金(U1833119);湖北省重点研发计划(2023BBB046)

Lightweight and improved apple orchard fruit recognition model CS_YOLOv7

OUYANG Yu(), LIU Shuo(), LI Mengmin, ZHANG Peng

School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430048, China

Received:2025-02-10 Online:2026-02-25 Published:2026-03-24

摘要/Abstract

摘要：

针对当前苹果园果实识别面临的模型参数规模庞大、计算资源消耗量过多、模型检测精度和检测速度难以实现良好平衡的问题,提出一种基于YOLOv7改进的轻量化模型CS_YOLOv7。首先,引入通道分离的高效层注意网络(CS_ELAN)和快速空间金字塔池化(SPPF)模块,实现模型整体轻量化;其次,采用K-means++算法生成适合本研究数据集的新先验框,增强模型定位目标的能力;然后,改用Wise-IoU损失函数代替原损失函数,减少低质量示例的有害梯度,提升模型收敛速度和目标识别的定位能力;最后,通过添加一种基于空间和通道维度的注意力机制SE_CBAM,使模型在更全局角度下提取苹果小目标的关键特征。结果表明:相较于原YOLOv7模型,改进模型的平均精确率(交并比为0.5,mAP@0.5)提升了1.7百分点,模型大小减少22.3 MB,检测速度提升118.9 frame·s^-1,模型参数量和计算量分别减少31.8%和16.1%。CS_YOLOv7模型在优化准确度的同时实现了多方位的轻量化,可应用于果园幼果数据集目标的快速识别,为今后高效的实时目标识别和后续的机器采摘奠定基础。

关键词: 苹果园, 果实识别, 机器视觉, 模型优化

Abstract:

Aiming at the problems faced by current fruit recognition in apple orchards, such as excessive model parameter scale, high computational resource consumption, and difficulty in achieving a good balance between model detection accuracy and speed, a lightweight improved model CS_YOLOv7 based on YOLOv7 was proposed. Firstly, the channel-split efficient layer aggregation network (CS_ELAN) and the spatial pyramid pooling fast (SPPF) module were introduced into the model to achieve overall lightweighting of the model. Secondly, the K-means++algorithm was adopted to generate new anchor boxes suitable for the dataset in this study, so as to enhance the model’s target localization capability. Thirdly, the Wise-IoU loss function was used to replace the original loss function, which reduced the harmful gradients of low-quality samples and improved the model convergence speed and target recognition localization accuracy. Finally, an attention mechanism SE_CBAM based on spatial and channel dimensions was added to enable the model to extract key features of small apple targets from a more global perspective. The results showed that, compared with the original YOLOv7 model, the improved model achieved a 1.7 percentage points increase in the mean average precision under the intersection over union of 0.5 (mAP@0.5), a reduction of 22.3 MB in model size, and an improvement of 118.9 frames·s^-1 in detection speed. Meanwhile, the number of model parameters and computational complexity decreased by 31.8% and 16.1%, respectively. The CS_YOLOv7 model achieves multi-dimensional lightweighting while optimizing accuracy, which can be applied to the rapid recognition of young fruits in orchard datasets, and lays a foundation for efficient real-time target recognition and subsequent robotic picking in the future.

Key words: apple orchard, fruit recognition, machine vision, model optimization

中图分类号:

S24
S661.1

欧阳宇, 刘朔, 李萌民, 张鹏. 轻量化改进的苹果园果实识别模型CS_YOLOv7[J]. 浙江农业学报, 2026, 38(2): 383-396.

OUYANG Yu, LIU Shuo, LI Mengmin, ZHANG Peng. Lightweight and improved apple orchard fruit recognition model CS_YOLOv7[J]. Acta Agriculturae Zhejiangensis, 2026, 38(2): 383-396.

图/表 14

图1 数据集增强示例 a,原图;b,随机裁剪;c,随机平移;d,随机翻转加高斯噪声;e,随机旋转加随机亮度。

Fig.1 Examples of dataset augmentation a, Original image; b, Random cropping; c, Random translation; d, Random flipping plus Gaussian noise; e, Random rotation plus random brightness.

图2 不同条件下的图像示例 a,逆光;b,顺光;c,果叶遮挡;d,灯杆遮挡;e,清晨初阳场景;f,中午盛阳场景;g,果实稀少场景;h,果实密集场景。

Fig.2 Examples of images under different conditions a, Backlighting; b, Frontlighting; c, Occlusion by fruit leaves; d, Occlusion by lampposts; e, Early morning sunrise scene; f, Midday strong sunlight scene; g, Sparse fruit scene; h, Dense fruit scene.

图3 CS_YOLOv7网络模型的整体结构 input,输入;backbone,骨干;neck,颈部;head,头部;cat,拼接;UpSample,上采样;Conv,卷积;MaxPool,最大池化;BN,批标准化;SiLU,激活函数;REP,重参数化模块;SE_CBAM,通道与空间注意力机制。下同。

Fig.3 Overall structure of CS-YOLOv7 network model cat, Concat; Conv, Convolution; MaxPool, Max pooling; BN, Batch normalization; SiLU, Sigmoid linear unit; REP, Re-parameterized convolution; SE_CBAM, Squeeze-and-excitation_convolutional block attention module. The same as below.

图4 CS_ELAN的结构 chunk,通道分离;add,逐元素相加。

Fig.4 Structure of CS_ELAN

表1 先验框重聚类

Table 1 Reunion of anchor box

检测层大小 Detection layer size	先验框尺寸Anchor box size
检测层大小 Detection layer size	调整前Before adjustment	调整后After adjustment
80×80	[12, 16], [19, 36], [40, 28]	[9, 12], [15, 29], [32, 23]
40×40	[36, 75], [76, 55], [72, 146]	[29, 60], [63, 44], [59, 119]
20×20	[142, 110], [192, 243], [459, 401]	[171, 132], [231, 292], [550, 483]

图5 SE_CBAM模块的结构 a,SE通道注意力模块;b,空间注意力模块;c,卷积注意力模块。

Fig.5 Structure of SE_CBAM module a, Squeeze-and-excitation channel attention module; b, Spatial attention module; c, Convolutional block attention module.

表2 先验框改进前后的性能对比

Table 2 Comparison of performance before and after the prior box reunion

模型Model	P	R	mAP@0.5
Y	73.5	67.4	73.4
G	73.5	69.3	74.5

图6 先验框改进前后的迭代曲线对比 mAP@0.5,平均精确率[交并比(IoU)为0.5时]。下同。Y代表原先验框,G代表改进的先验框。

Fig.6 Comparison of iteration curves before and after the prior box reunion mAP@0.5,Mean average precision under intersection over union (IoU) of 0.5. The same as below. Y represents the model with the original anchor boxes, and G represents the model with the improved anchor boxes.

表3 不同损失函数下模型的性能对比

Table 3 Comparison of model performance under different loss functions

损失函数 Loss function	P/%	R/%	mAP@0.5/%	v/(frame·s^-1)
CIoU	73.5	69.3	74.5	106.3
EIoU	67.3	67.3	70.2	166.6
SIoU	68.3	59.3	65.0	169.4
MPDIoU	79.2	63.9	74.5	147.0
Wise-IoU	80.8	68.7	79.1	161.3

图7 不同损失函数的迭代曲线

Fig.7 Iteration curves under different loss functions

表4 消融实验的结果

Table 4 Results of ablation experiments

编号 No.	改进情况Improvement status				N	v/ (frame·s^-1)	mAP@0.5/%
编号 No.	CS_ELAN+SPPF	K-means++	Wise-IoU	SE_CBAM	N	v/ (frame·s^-1)	mAP@0.5/%
1	×	×	×	×	37 196 556	32.6	78.3
2	√	×	×	×	25 226 060	166.6	73.4
3	√	√	×	×	25 226 060	106.3	74.5
4	√	×	√	×	25 226 060	166.6	71.3
5	√	√	√	×	25 226 060	161.3	79.1
6	√	√	√	√	25 367 426	151.5	80.0

表5 不同模型的目标检测效果对比

Table 5 Comparison of target detection effects of different models

模型	N	FLOPS/10⁹	v/(frame·s^-1)	mAP@0.5/%	S/MB
SSD	24 386 000	87.5	82.3	65.2	100.0
Faster R-CNN	41 753 000	83.8	55.0	65.7	314.0
RetinaNet	32 201 069	127.2	50.0	76.1	245.0
YOLOv4	63 937 686	170.0	70.0	71.6	244.0
YOLOv5x	87 244 374	217.0	51.0	81.3	1 000.0
YOLOv7	37 196 556	105.0	32.6	78.3	71.0
YOLOv8	25 902 640	79.3	101.9	78.4	49.6
YOLOv9	25 590 912	104.0	79.2	79.5	49.2
Tiny-YOLO	6 014 988	13.2	588.2	72.7	6.3
MobileNet-YOLO	24 616 556	41.3	161.0	78.1	49.7
YOLOv12	26 454 880	89.7	107.5	80.2	53.5
CS_YOLOv7	25 367 426	88.1	151.5	80.0	48.7

图8 不同光照条件下各模型对苹果幼果的识别效果图中紫色圆圈框为漏检或错检的苹果幼果,清晨初阳和中午盛阳是同一地点果树在不同时间段拍摄的照片。

Fig.8 Identification effect of young apple fruit under different lighting conditions by different models The purple circles in the picture indicate missed or false detections of young apple fruits. The scenes “Early morning sunrise” and “Midday strong sunlight” were photographed at the same location of the fruit tree at different times.

表6 各模型在不同数据集上的检测效果对比

Table 6 Comparison of detection effects on different datasets by different models

数据集 Dataset	模型 Model	P	R	mAP@0.5
子集1	YOLOv7	76.0	78.0	74.0
Subset	MobileNet-YOLO	84.6	73.4	70.0
	YOLOv12	96.0	61.3	77.4
	CS_YOLOv7	78.6	76.7	77.8
子集2	YOLOv7	85.4	71.4	67.8
Subset 2	MobileNet-YOLO	92.0	69.1	69.2
	YOLOv12	89.5	60.4	76.9
	CS_YOLOv7	82.7	77.7	74.1

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轻量化改进的苹果园果实识别模型CS_YOLOv7

Lightweight and improved apple orchard fruit recognition model CS_YOLOv7

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