RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments
Accurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations, we propose a Multi-scale Adaptive Real-time Dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/996 |
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| author | Baoshan Sun Haolin Tang Liqing Gao Kaiyu Bi Jiabao Wen |
| author_facet | Baoshan Sun Haolin Tang Liqing Gao Kaiyu Bi Jiabao Wen |
| author_sort | Baoshan Sun |
| collection | DOAJ |
| description | Accurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations, we propose a Multi-scale Adaptive Real-time Detector (RTDETR-MARD) based on RT-DETR that introduces three key innovations for improved floating waste detection using unmanned surface vessels (USVs). First, our hierarchical multi-scale feature integration leverages the gather-and-distribute mechanism to enhance feature aggregation and cross-layer interaction. Second, we develop an advanced feature fusion module incorporating feature alignment, Information Fusion, information injection, and Scale Sequence Feature Fusion components to ensure precise spatial alignment and semantic consistency. Third, we implement the Wise-IoU loss function to optimize localization accuracy through high-quality anchor supervision. Extensive experiments demonstrate the framework’s effectiveness, achieving state-of-the-art performance of 86.6% mAP50 at 96.8 FPS on the FloW dataset and 49.2% mAP50 at 107.5 FPS on our custom water surface waste dataset. These results confirm RTDETR-MARD’s superior accuracy, real-time capability, and robustness across diverse environmental conditions, making it particularly suitable for practical deployment in ecological monitoring systems where both speed and precision are critical requirements. |
| format | Article |
| id | doaj-art-e2b269bf31e2432daac929f079d61d1d |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e2b269bf31e2432daac929f079d61d1d2025-08-20T03:47:54ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113599610.3390/jmse13050996RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic EnvironmentsBaoshan Sun0Haolin Tang1Liqing Gao2Kaiyu Bi3Jiabao Wen4School of Computer Science and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Computer Science and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Computer Science and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Computer Science and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300052, ChinaAccurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations, we propose a Multi-scale Adaptive Real-time Detector (RTDETR-MARD) based on RT-DETR that introduces three key innovations for improved floating waste detection using unmanned surface vessels (USVs). First, our hierarchical multi-scale feature integration leverages the gather-and-distribute mechanism to enhance feature aggregation and cross-layer interaction. Second, we develop an advanced feature fusion module incorporating feature alignment, Information Fusion, information injection, and Scale Sequence Feature Fusion components to ensure precise spatial alignment and semantic consistency. Third, we implement the Wise-IoU loss function to optimize localization accuracy through high-quality anchor supervision. Extensive experiments demonstrate the framework’s effectiveness, achieving state-of-the-art performance of 86.6% mAP50 at 96.8 FPS on the FloW dataset and 49.2% mAP50 at 107.5 FPS on our custom water surface waste dataset. These results confirm RTDETR-MARD’s superior accuracy, real-time capability, and robustness across diverse environmental conditions, making it particularly suitable for practical deployment in ecological monitoring systems where both speed and precision are critical requirements.https://www.mdpi.com/2077-1312/13/5/996real-time object detectionfloating waste monitoringadaptive feature aggregationmulti-scale feature fusion |
| spellingShingle | Baoshan Sun Haolin Tang Liqing Gao Kaiyu Bi Jiabao Wen RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments Journal of Marine Science and Engineering real-time object detection floating waste monitoring adaptive feature aggregation multi-scale feature fusion |
| title | RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments |
| title_full | RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments |
| title_fullStr | RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments |
| title_full_unstemmed | RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments |
| title_short | RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments |
| title_sort | rtdetr mard a multi scale adaptive real time framework for floating waste detection in aquatic environments |
| topic | real-time object detection floating waste monitoring adaptive feature aggregation multi-scale feature fusion |
| url | https://www.mdpi.com/2077-1312/13/5/996 |
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