Lightweight detection of small target diseases in apple leaf using improved YOLOv5s
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摘要:
为解决自然环境中苹果叶片病害检测场景复杂、小目标病害检测难度高以及模型参数大无法在移动端和嵌入式设备部署等问题，提出一种基于YOLOv5s的苹果叶片小目标病害轻量化检测方法。该方法将YOLOv5s的骨干网络更改为ShuffleNet v2轻量化网络，引入CBAM（convolutional block attention module）注意力模块使模型关注苹果叶片小目标病害，添加改进RFB-s（receptive field block-s）支路获取多尺度特征，提高苹果叶片病害检测精度，并更改边界框回归损失函数为SIoU（scylla-intersection over union），增强病斑定位能力。试验表明改进后的YOLOv5s模型在IoU大于0.5时的平均精度均值（mean average precision，mAP0.5）和每秒传输帧数（frame per second，FPS）分别达到90.6%和175帧/s，对小目标的平均检测准确率为38.2%，与基准模型YOLOv5s相比，其mAP0.5提升了0.8个百分点，参数量减少了6.17 MB，计算量减少了13.8 G，对小目标的检测准确率提高了3个百分点。改进后的YOLOv5s目标检测模型与Faster R-CNN、SSD、YOLOv5m、YOLOv7、YOLOv8和YOLOv5s目标检测模型相比，具有最小的参数量和计算量，对小目标病害叶斑病和锈病的检测准确率分别提高了1.4、4.1、0.5、5.7、3.5、3.9和1.5、4.3、1.2、2.1、4.0、2.6个百分点，该方法为真实自然环境下苹果叶片病害尤其是小目标病害的轻量化检测提供参考依据。
Abstract:Leaf diseases have seriously threatened the quality and yield of apple fruits. Efficient and accurate identification of apple leaf diseases is of great significance for refined orchard management. However, it is difficult to detect apple leaf disease at the early stage, due to the small target disease lesion and complex scenarios. Furthermore, the parameters of the detection model are too large to deploy on mobile terminals or embedded devices. In this study, a lightweight detection was proposed to detect the small apple leaf disease using improved You Only Look Once Version 5s (YOLOv5s). Firstly, the ShuffleNet v2 lightweight network was employed as the backbone network of YOLOv5s, in order to reduce the parameters and float-point operations per second (FLOPs) of the new network. Secondly, the Convolutional Block Attention Module (CBAM) was adopted to focus on the features of the small target disease of apple leaves. High performance was improved in the network information transmission and the sensitivity of the model to features, and then combined the spatial and channels to enhance the cross-channel interaction of the model. Thirdly, an improved Receptive Field Block-s (RFB-s) branch was added to obtain the multi-scale features for the high feature extraction of the model and the detection accuracy of apple leaf disease. Finally, SCYLLA-Intersection over Union (SIoU) was selected as the loss function of the bounding box regression. The performance of disease spot localization was enhanced for the high accuracy and training speed of the model. The experimental results demonstrated that the mAP0.5 and FPS of the improved YOLOv5s reached 90.6% and 175 frames/s, respectively. The mAP0.5 of the improved model increased by 0.8%, while the number of parameters was reduced by 6.17 MB, and the calculation amount was reduced by 13.8 G FLOPs, compared with the baseline model YOLOv5s. Particularly, the average precision of small disease targets was up to 38.2%, indicating the high efficiency and robustness of the model. Moreover, the mAP0.5 were 2.0, 1.4, 2.0 and 9.4 percentage points higher in the improved model YOLOv5s, respectively, and the average accuracy rate of small disease targets increased by 1.5, 2.0, 1.8 and 2.1 percentage points, respectively, compared with Squeeze-and-Excitation (SE), Efficient Channel Attention (ECA), CoordAttention (CA), and Non-local neural network in the CBAM attention module. The SIoU bounding box loss function presented the highest detection accuracy, compared with the DIoU, CIoU, and GIoU. In addition, the improved YOLOv5 object detection model displayed the minimum number of parameters and calculations. Specifically, the detection accuracy of frog eye leaf spot and rust diseases increased by 1.4, 4.1, 0.5, 5.7, 3.5, 3.9 and 1.5, 4.3, 1.2, 2.1, 4.0, 2.6 percentage points, respectively, compared with similar object detection models, such as Faster R-CNN, SSD, YOLOv5m, YOLOv7, YOLOv8, and YOLOv5s. The improved model can be expected to detect the small target diseases in complex environments of the actual field, particularly for a variety of diseases in apple leaves at the same time. The findings can provide a strong reference for the lightweight detection of apple leaf diseases in real natural environments, especially small-target diseases.
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Keywords:
- disease /
- deep learning /
- object detection /
- apple leaf /
- YOLOv5s
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图 2 改进YOLOv5s网络
注：Conv为卷积操作，BN为批标准化，SiLU和ReLU为激活函数，add为像素相加函数，Concat为通道数相加函数，Upsample为上采样操作，Shuffle_CBAM是包含CBAM的ShuffleNet模块，Shuffle_Block是基本的ShuffleNet模块，RFB-s为添加的模块。
Figure 2. Improved YOLOv5s network
Note: Conv is a convolution operation, BN is batch normalization, SiLU and ReLU are activation functions, add represents element-wise addition, Concat is channel-wise addition, Upsample stands for feature upsample, Shuffle_CBAM is a ShuffleNet module containing CBAM, Shuffle_Block is the basic ShuffleNet module, RFB-s indicates the newly added module.
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图 3 ShuffleNet v2轻量化网络模型
注：DWConv为深度卷积。
Figure 3. ShuffleNet v2 lightweight network model
Note: DWConv is depthwise convolution.
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图 5 改进的RFB-s模块
注：Rate为空洞卷积的扩张率。
Figure 5. Improved RFB-s module
Note: Rate is the dilation rate of the dilated convolution.
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图 7 基于 YOLOv5s 与本文检测苹果叶片病害结果对比
注：方框内为检测到的病害，方框上方为检测到的病害名称及病害概率，箭头指向的病害为YOLOv5s未检测到的病害。
Figure 7. Comparison of test results of apple leaf diseases based on YOLOv5s and our paper
Note：The boxes indicate the detected diseases, the top of the box indicates the name and probability of the detected diseases, and the diseases pointed by the arrows are those not detected by YOLOv5s.
表 1 苹果叶片病害数据集
Table 1 The dataset of apple leaf disease
病害种类
Disease type图片数量
Number of images来源
Source of images训练集
Training set增强后的训练集
Enhanced training set验证集
Validation set测试集
Test set黑星病Scab 915 CVPR2021 FGVC8 733 733 91 91 叶斑病 Frogeye leaf spot 952 CVPR2021 FGVC8 762 762 95 95 锈病 Rust 928 CVPR2021 FGVC8 743 743 93 92 白粉病 Powdery mildew 903 CVPR2021 FGVC8 723 723 90 90 花叶病 Mosaic 378 拍照+飞桨AI Studio 190 760 94 94 黑星病+叶斑病Scab + frogeye leaf spot 54 CVPR2021 FGVC8 33 66 11 10 锈病+叶斑病Rust + frogeye leaf spot 52 CVPR2021 FGVC8 32 64 10 10 总计 Total number 4 182 3 216 3 851 484 482 
下载: 导出CSV
表 2 数据集中小目标的计算
Table 2 Calculation of small targets in the dataset
尺度比值
Ratio of scale黑星病
Scab叶斑病
Frogeye
leaf spot锈病
Rust白粉病
Powdery
mildew花叶病 Mosaic 病害目标宽度与原
始图像宽度的比值
Ratio of the disease target width
to the original image width0.75 0.04 0.05 0.61 0.61 病害目标高度与原
始图像高度的比值
Ratio of the disease target
height to the original image height0.71 0.07 0.08 0.59 0.68 注：病害目标指感兴趣的病害。 Note: The disease target refers to the disease of interest.
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表 3 网络轻量化及小目标消融试验
Table 3 Network lightweight and small target ablation experiment
ShuffleNet v2
轻量化网络
ShuffleNet v2
lightweight
networkCBAM
注意力模块
CBAM
attention
module改进的
RFB-s结构
Improved
RFB-s
structureSIoU损
失函数
SIoU loss
functionmAP0.5/% 参数量
Parameters/
MB浮点计算数
Floating-point
operations
per second,
FLOPs/G网络
层数
Network
layers帧率
Frames
per second,
FPS/
(帧·s−1)小目标的
平均精度
Average
precision for
small targets
APS/%中目标的
平均精度
Average
precision
for medium
targets
APM/%大目标的
平均精度
Average
precision
for large
targets
APL /%− − − − 89.8 7.02 15.8 157 185 35.2 39.5 59.6 √ − − − 88.1 0.84 1.8 169 232 35.9 49.5 51.8 √ √ − − 89.6 0.82 1.7 187 196 37.5 36.0 52.4 √ − √ − 88.4 0.87 2.2 225 192 35.6 46.5 50.1 √ √ √ − 90.3 0.85 2.0 243 172 38.1 40.6 53.6 √ √ √ √ 90.6 0.85 2.0 243 175 38.2 48.3 52.9 注：“√” 表示使用此模块，“−” 表示不使用此模块，mAP0.5表示IoU大于0.5时预测正确的平均精度均值。
Note："√" represents use this module, "−" represents do not use this module, mAP0.5 represents the mean average precision obtained from the correct prediction when IoU is greater than 0.5.
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表 4 不同注意力机制的消融试验
Table 4 Ablation experiment of different attention mechanisms
注意力机制
Attention mechanismsmAP0.5/% 准确率
Precision/%召回率
Recall/%F1值
F1 score/%参数量
Parameters/MBFLOPs/G FPS/(帧·s−1) APS/% APM/% APL/% SE 88.6 79.9 84.8 82 0.83 2.0 192 36.7 40.2 51.5 ECA 89.2 81.0 86.3 83 0.82 2.0 192 36.2 48.4 47.1 CA 88.6 80.0 86.2 83 0.83 2.0 172 36.4 39.0 50.0 Non-local 81.2 71.9 84.3 77 0.91 2.5 94 36.1 45.4 38.1 CBAM 90.6 81.8 86.5 84 0.85 2.0 175 38.2 48.3 52.9
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表 5 不同检测模型性能对比
Table 5 Performance comparison of different detection models
模型
ModelmAP0.5/% 参数量
Parameters /MBFLOPs/G 准确率precision/% 叶斑病Frogeye leaf spot 白粉病Powdery mildew 锈病识Rust 黑星病Scab 花叶病Mosaic Faster R-CNN 90.9 60.14 121.45 85.8 98.8 82.9 95.0 92.0 SSD 88.9 24.28 137.97 83.1 96.4 80.1 93.8 91.1 YOLOv5m 91.1 20.86 47.90 86.7 98.2 83.2 94.7 92.7 YOLOv7 88.1 6.02 6.56 81.5 92.9 82.3 94.7 88.9 YOLOv8 88.0 11.13 14.28 83.7 94.5 80.4 92.2 88.9 YOLOv5s 89.8 7.02 15.80 83.3 98.0 81.8 94.4 91.6 改进模型
Improved YOLOv590.6 0.85 2.0 87.2 96.2 84.4 94.0 91.3
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