| 基于改进YOLOv5s的日光温室损伤等级遥感影像检测方法 |
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| 引用本文： | 何斌,张金豪,丁小明,付国,李雯雯.基于改进YOLOv5s的日光温室损伤等级遥感影像检测方法[J].农业工程学报,2023,39(22):133-143. |
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| 作者姓名： | 何斌 张金豪 丁小明 付国 李雯雯 |
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| 作者单位： | 西北农林科技大学水利与建筑工程学院, 杨凌 712100;西北农林科技大学旱区农业水土工程教育部重点实验室, 杨凌 712100;农业农村部规划设计研究院设施农业研究所, 北京 100125 |
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| 基金项目： | 陕西省科技创新引导专项项目（2021QFY08-01） |
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| 摘 要： | 针对目前日光温室损伤程度的统计方法普遍依靠人工目视导致的检测效率低、耗时长、精确度低等问题，该研究提出了一种基于改进YOLOv5s的日光温室损伤等级遥感影像检测模型。首先，采用轻量级MobileNetV3作为主干特征提取网络，减少模型的参数量；其次，利用轻量级的内容感知重组特征嵌入模块（content aware reassembly feature embedding，CARAFE）更新模型的上采样操作，增强特征信息的表达能力，并引入显式视觉中心块（explicit visual center block，EVCBlock）替换和更新颈部层，进一步提升检测精度；最后将目标边界框的原始回归损失函数替换为EIoU（efficient intersection over union）损失函数，提高模型的检测准确率。试验结果表明，与基准模型相比，改进后模型的参数数量和每秒浮点运算次数分别减少了17.91和15.19个百分点，准确率和平均精度均值分别提升了0.4和0.8个百分点；经过实地调查，该模型的平均识别准确率为84.00%，优于Faster R-CNN、SSD、Centernet、YOLOv3等经典目标检测算法。日光温室损伤等级快速识别方法可以快速检测日光温室的数量、损伤等级等信息，减少设施农业管理中的人力成本，为现代化设施农业的建设、管理和改造升级提供信息支持。
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| 关 键 词： | 温室 遥感 深度学习 设施农业 YOLOv5 快速检测 |
| 收稿时间： | 2023/7/30 0:00:00 |
| 修稿时间： | 2023/10/24 0:00:00 |
Detecting damage levels of solar greenhouses from remote sensing images using improved YOLOv5s |
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| HE Bin,ZHANG Jinhao,DING Xiaoming,FU Guo,LI Wenwen.Detecting damage levels of solar greenhouses from remote sensing images using improved YOLOv5s[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(22):133-143. |
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| Authors: | HE Bin ZHANG Jinhao DING Xiaoming FU Guo LI Wenwen |
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| Affiliation: | College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, Shannxi 712100, China;Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Yangling, Shannxi 712100, China;Institute of Protected Agriculture, Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China |
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| Abstract: | Solar greenhouses have been widely used in modern precision agriculture in China. However, potential negative environmental impacts can also be introduced, including atmospheric pollution, soil contamination, and water pollution. Great challenges have been posed on the rational management of farmlands and resource utilization. Meanwhile, the manual collection of visual data cannot fully meet the rapid acquisition of information from large-scale greenhouse facilities. In this study, Chinese solar greenhouses (CSGs) were focused on detecting the damage levels from the remote sensing images using an improved YOLO-DLSG (YOLO-damage level of solar greenhouse) object detection model. Comparative experiments were conducted using high-resolution solar greenhouse remote sensing image datasets. A dataset was collected with the 3235 images of solar greenhouses and then subjected to data augmentation. The classification standards were also combined with the damage indicators specific to solar greenhouses. The damage levels were then established from the high-resolution remote sensing satellite images. The YOLOv5s lightweight improvement was employed to reconstruct the YOLOv5s backbone with the lightweight MobileNetV3 architecture. This restructure reduced the channel dimensions and network parameter volume. Furthermore, the upsampling operator in the neck layers was replaced and then updated. A lightweight upsampling operator was selected as CARAFE (content-aware reassembly feature embedding), using an upsampled kernel prediction module. The feature recombination module was reconstructed in the upsample structure. EVCBlock (explicit visual center block) was utilized to further reduce the computational complexity for the high accuracy of detection on rotating objects. Finally, the CIoU (complete intersection over union) loss function for the target bounding box regression was replaced with the EIoU (Enhanced Intersection over Union) loss, in order to accelerate the convergence of the prediction box regression loss function for the high accuracy of prediction box regression. The experimental results show that the improved model reduced the number of parameters and floating-point operations per second by 17.91 and 15.19 percentage points, with an increase of 0.4 and 0.8 percentage points in the precision and mean average precision, respectively. Compared with the current mainstream lightweight models, such as SSD, Faster R-CNN, Centernet, YOLOv3-spp, and YOLOv3-tiny, the improved YOLO-DLSG model demonstrated superior performance, in terms of accuracy, recall, detection speed, and model lightweight. Data collection and accuracy validation were conducted in the Yangling Agricultural Demonstration Zone in Shaanxi Province of China. An average recognition accuracy of 84.00% was achieved in the improved model. A challenging task was to determine the distribution, quantity, and extent of greenhouse damage in a cost-effective, efficient, and user-friendly manner. This finding can provide a simple and effective way for the rapid location and automatic quantity extraction of solar greenhouses with different damage levels. |
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| Keywords: | greenhouse remote sensing deep learning facility agriculture YOLOv5 rapid detection |
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