Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head
文献类型：期刊论文
| 作者 | Qiu, Mulan4; Huang, Liang1,3,4; Tang, Bo-Hui2,4 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2023-12-31 |
| 卷号 | 16期号:1页码:113-129 |
| ISSN号 | 1753-8947 |
| 关键词 | HSRRIs bridge detection BiFPN CBAM feature fusion decoupled head |
| DOI | 10.1080/17538947.2022.2163514 |
| 通讯作者 | Huang, Liang(kmhuangliang@kust.edu.cn) |
| 英文摘要 | The different imaging conditions of high spatial resolution remote sensing images (HSRRSIs) tend to cause large differences in the background information of bridges from the images, including problems of difficult detection of multiscale bridges, leakage of small bridges and insufficient detection accuracy for their detection. To address these problems, a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper. First, the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network (BiFPN). Then, the convolutional block attention module (CBAM) is fused into the three effective feature layers after feature pyramid network processing. The bridge feature information is effectively extracted from the channel and spatial dimensions. Next, the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously. Finally, the practical effect is evaluated by calculating the average precision (AP). According to the experimental results, the AP of the proposed method is 98.1%, which is improved by 4.1%similar to 23.5% compared with other models. |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:000906650500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/188668]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Liang |
| 作者单位 | 1.Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming 650093, Yunnan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Yunnan, Peoples R China 4.Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu, Mulan,Huang, Liang,Tang, Bo-Hui. Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2023,16(1):113-129. |
| APA | Qiu, Mulan,Huang, Liang,&Tang, Bo-Hui.(2023).Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head.INTERNATIONAL JOURNAL OF DIGITAL EARTH,16(1),113-129. |
| MLA | Qiu, Mulan,et al."Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head".INTERNATIONAL JOURNAL OF DIGITAL EARTH 16.1(2023):113-129. |
入库方式： OAI收割
来源：地理科学与资源研究所
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