An Improved Instance Segmentation Approach for Solid Waste Retrieval with Precise Edge from UAV Images
文献类型：期刊论文
| 作者 | Huang, Yaohuan1,2; Chen, Zhuo1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-10-11 |
| 卷号 | 17期号:20页码:3410 |
| 关键词 | solid waste deep learning instance segmentation Mask R-CNN precise edge |
| DOI | 10.3390/rs17203410 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Highlights What are the main findings? Proposes WMNet-SW, which fuses an improved Mask R-CNN (with anchor/RoI optimization and a Layer Feature Aggregation mask head) with the watershed transform to retrieve precise-edge SW from high-resolution UAV images. Outperforms baseline deep learning model, captures fine edge details and even mitigates limitations in training GT. What is the implication of the main finding? Provides a practical solution for retrieving precise-edge SW from UAV imagery, contributing to the protection of regional environment and ecosystem health.Highlights What are the main findings? Proposes WMNet-SW, which fuses an improved Mask R-CNN (with anchor/RoI optimization and a Layer Feature Aggregation mask head) with the watershed transform to retrieve precise-edge SW from high-resolution UAV images. Outperforms baseline deep learning model, captures fine edge details and even mitigates limitations in training GT. What is the implication of the main finding? Provides a practical solution for retrieving precise-edge SW from UAV imagery, contributing to the protection of regional environment and ecosystem health.Abstract As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid waste detection. However, manually interpreting solid waste in UAV images is inefficient, and object detection methods encounter serious challenges due to the patchy distribution, varied textures and colors, and fragmented edges of solid waste. In this study, we proposed an improved instance segmentation approach called Watershed Mask Network for Solid Waste (WMNet-SW) to accurately retrieve solid waste with precise edges from UAV images. This approach combined the well-established Mask R-CNN segmentation framework with the watershed transform edge detection algorithm. The benchmark Mask R-CNN was improved by optimizing the anchor size and Region of Interest (RoI) and integrating a new mask head of Layer Feature Aggregation (LFA) to initially detect solid waste. Subsequently, edges of the detected solid waste were precisely adjusted by overlaying the segments generated by the watershed transform algorithm. Experimental results show that WMNet-SW significantly enhances the performance of Mask R-CNN in solid waste retrieval, increasing the average precision from 36.91% to 58.10%, F1-score from 0.5 to 0.65, and AP from 63.04% to 64.42%. Furthermore, our method efficiently detects the details of solid waste edges, even overcoming the limitations of training Ground Truth (GT). This study provides a solution for retrieving solid waste with precise edges from UAV images, thereby contributing to the protection of the regional environment and ecosystem health. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001603010900001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217790]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Huang, Yaohuan |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Yaohuan,Chen, Zhuo. An Improved Instance Segmentation Approach for Solid Waste Retrieval with Precise Edge from UAV Images[J]. REMOTE SENSING,2025,17(20):3410. |
| APA | Huang, Yaohuan,&Chen, Zhuo.(2025).An Improved Instance Segmentation Approach for Solid Waste Retrieval with Precise Edge from UAV Images.REMOTE SENSING,17(20),3410. |
| MLA | Huang, Yaohuan,et al."An Improved Instance Segmentation Approach for Solid Waste Retrieval with Precise Edge from UAV Images".REMOTE SENSING 17.20(2025):3410. |
入库方式： OAI收割
来源：地理科学与资源研究所
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