| 基于改进YOLOv5s的苹果叶片小目标病害轻量化检测方法 |
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| 引用本文： | 公徐路,张淑娟.基于改进YOLOv5s的苹果叶片小目标病害轻量化检测方法[J].农业工程学报,2023,39(19):175-184. |
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| 作者姓名： | 公徐路 张淑娟 |
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| 作者单位： | 山西农业大学农业工程学院, 太谷 030801;山西农业大学软件学院, 太谷 030801 |
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| 基金项目： | 山西省重点研发项目（No. 201903D221027）；教育部产学合作协同育人项目（No.220504309235356） |
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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、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。
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| 关 键 词： | 病害 深度学习 目标检测 苹果叶片 YOLOv5s |
| 收稿时间： | 2023/6/18 0:00:00 |
| 修稿时间： | 2023/9/6 0:00:00 |
Lightweight detection of small target diseases in apple leaf using improved YOLOv5s |
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| GONG Xulu,ZHANG Shujuan.Lightweight detection of small target diseases in apple leaf using improved YOLOv5s[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(19):175-184. |
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| Authors: | GONG Xulu ZHANG Shujuan |
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| Institution: | College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China;School of Software, Shanxi Agricultural University, Taigu 030801, China |
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| 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 6.17MB, and the calculation amount was reduced by 13.8G 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, 1.4, 2 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, 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, 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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