PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning
文献类型：期刊论文
| 作者 | Feng, Junning2; Wang, Dongliang1; Yang, Fan2; Huang, Jing2; Wang, Minghao2; Tao, Mengfan2; Chen, Wei2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 14期号:19页码:21 |
| 关键词 | greenhouse area extraction quantity estimation target detection semantic segmentation |
| DOI | 10.3390/rs14195064 |
| 通讯作者 | Wang, Dongliang(wangdongliang@igsnrr.ac.cn) |
| 英文摘要 | The rapid boom of the global population is causing more severe food supply problems. To deal with these problems, the agricultural greenhouse is an effective way to increase agricultural production within a limited space. To better guide agricultural activities and respond to future food crises, it is important to obtain both the agricultural greenhouse area and quantity distribution. In this study, a novel dual-task algorithm called Pixel-based and Object-based Dual-task Detection (PODD) that combines object detection and semantic segmentation is proposed to estimate the quantity and extract the area of agricultural greenhouses based on RGB remote sensing images. This algorithm obtains the quantity of agricultural greenhouses based on the improved You Only Look Once X (YOLOX) network structure, which is embedded with Convolutional Block Attention Module (CBAM) and Adaptive Spatial Feature Fusion (ASFF). The introduction of CBAM can make up for the lack of expression ability of its feature extraction layer to retain more important feature information. Adding the ASFF module can make full use of the features in different scales to increase the precision. This algorithm obtains the area of agricultural greenhouses based on the DeeplabV3+ neural network using ResNet-101 as a feature extraction network, which not only effectively reduces hole and plaque issues but also extracts edge details. Experimental results show that the mAP and F1-score of the improved YOLOX network reach 97.65% and 97.50%, 1.50% and 2.59% higher than the original YOLOX solution. At the same time, the accuracy and mIoU of the DeeplabV3+ network reach 99.2% and 95.8%, 0.5% and 2.5% higher than the UNet solution. All of the metrics in the dual-task algorithm reach 95% and even higher. Proving that the PODD algorithm could be useful for agricultural greenhouse automatic extraction (both quantity and area) in large areas to guide agricultural policymaking. |
| WOS关键词 | OBJECT-BASED CLASSIFICATION ; PLASTIC GREENHOUSE ; FRAMEWORK ; IMAGERY ; AREA |
| 资助项目 | National Key R&D Program of China[2021YFF0704400] ; Undergraduate Training Program for Innovation and Entrepreneurship of CUMTB[202102010] ; National Science Foundation of China[41501416] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000867280900001 |
| 资助机构 | National Key R&D Program of China ; Undergraduate Training Program for Innovation and Entrepreneurship of CUMTB ; National Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185521]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Dongliang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Junning,Wang, Dongliang,Yang, Fan,et al. PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning[J]. REMOTE SENSING,2022,14(19):21. |
| APA | Feng, Junning.,Wang, Dongliang.,Yang, Fan.,Huang, Jing.,Wang, Minghao.,...&Chen, Wei.(2022).PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning.REMOTE SENSING,14(19),21. |
| MLA | Feng, Junning,et al."PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning".REMOTE SENSING 14.19(2022):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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