MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance
The detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. However, due to the scarcity of appearance information, minimal pixel representation, susceptibility to blending with the background, and the incompatibility of convent...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4485 |
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| author | Nan Su Zilong Zhao Yiming Yan Jinpeng Wang Wanxuan Lu Hongbo Cui Yunfei Qu Shou Feng Chunhui Zhao |
| author_facet | Nan Su Zilong Zhao Yiming Yan Jinpeng Wang Wanxuan Lu Hongbo Cui Yunfei Qu Shou Feng Chunhui Zhao |
| author_sort | Nan Su |
| collection | DOAJ |
| description | The detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. However, due to the scarcity of appearance information, minimal pixel representation, susceptibility to blending with the background, and the incompatibility of conventional metrics, the rapid and accurate detection of tiny objects poses significant challenges. To address these issues, a single-stage tiny object detector tailored for aerial imagery is proposed, comprising two primary components. Firstly, we introduce a light backbone-heavy neck architecture, named the Global Context Self-Attention and Dense Nested Connection Feature Extraction Network (GC-DN Network), which efficiently extracts and fuses multi-scale features of the target. Secondly, we propose a novel metric, MMPW, to replace the Intersection over Union (IoU) in label assignment strategies, Non-Maximum Suppression (NMS), and regression loss functions. Specifically, MMPW models bounding boxes as 2D Gaussian distributions and utilizes the Mixed Minimum Point-Wasserstein Distance to quantify the similarity between boxes. Experiments conducted on the latest aerial image tiny object datasets, AI-TOD and VisDrone-19, demonstrate that our method improves AP50 performance by 9.4% and 5%, respectively, and AP performance by 4.3% and 3.6%. This validates the efficacy of our approach for detecting tiny objects in aerial imagery. |
| format | Article |
| id | doaj-art-bd5f903ef2b1477da62c90c161f4c2df |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bd5f903ef2b1477da62c90c161f4c2df2025-08-20T01:55:27ZengMDPI AGRemote Sensing2072-42922024-11-011623448510.3390/rs16234485MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein DistanceNan Su0Zilong Zhao1Yiming Yan2Jinpeng Wang3Wanxuan Lu4Hongbo Cui5Yunfei Qu6Shou Feng7Chunhui Zhao8College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaThe Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaSchool of Light Industry, Harbin University of Commerce, Harbin 150028, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaThe detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. However, due to the scarcity of appearance information, minimal pixel representation, susceptibility to blending with the background, and the incompatibility of conventional metrics, the rapid and accurate detection of tiny objects poses significant challenges. To address these issues, a single-stage tiny object detector tailored for aerial imagery is proposed, comprising two primary components. Firstly, we introduce a light backbone-heavy neck architecture, named the Global Context Self-Attention and Dense Nested Connection Feature Extraction Network (GC-DN Network), which efficiently extracts and fuses multi-scale features of the target. Secondly, we propose a novel metric, MMPW, to replace the Intersection over Union (IoU) in label assignment strategies, Non-Maximum Suppression (NMS), and regression loss functions. Specifically, MMPW models bounding boxes as 2D Gaussian distributions and utilizes the Mixed Minimum Point-Wasserstein Distance to quantify the similarity between boxes. Experiments conducted on the latest aerial image tiny object datasets, AI-TOD and VisDrone-19, demonstrate that our method improves AP50 performance by 9.4% and 5%, respectively, and AP performance by 4.3% and 3.6%. This validates the efficacy of our approach for detecting tiny objects in aerial imagery.https://www.mdpi.com/2072-4292/16/23/4485tiny object detectionaerial imageryMMPW distanceglobal context self-attentiondense nested connection |
| spellingShingle | Nan Su Zilong Zhao Yiming Yan Jinpeng Wang Wanxuan Lu Hongbo Cui Yunfei Qu Shou Feng Chunhui Zhao MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance Remote Sensing tiny object detection aerial imagery MMPW distance global context self-attention dense nested connection |
| title | MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance |
| title_full | MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance |
| title_fullStr | MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance |
| title_full_unstemmed | MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance |
| title_short | MMPW-Net: Detection of Tiny Objects in Aerial Imagery Using Mixed Minimum Point-Wasserstein Distance |
| title_sort | mmpw net detection of tiny objects in aerial imagery using mixed minimum point wasserstein distance |
| topic | tiny object detection aerial imagery MMPW distance global context self-attention dense nested connection |
| url | https://www.mdpi.com/2072-4292/16/23/4485 |
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