DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR
For drone-based detection tasks, accurately identifying small-scale targets like people, bicycles, and pedestrians remains a key challenge. In this paper, we propose DV-DETR, an improved detection model based on the Real-Time Detection Transformer (RT-DETR), specifically optimized for small target d...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7376 |
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| _version_ | 1846152388378886144 |
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| author | Xiaolong Wei Ling Yin Liangliang Zhang Fei Wu |
| author_facet | Xiaolong Wei Ling Yin Liangliang Zhang Fei Wu |
| author_sort | Xiaolong Wei |
| collection | DOAJ |
| description | For drone-based detection tasks, accurately identifying small-scale targets like people, bicycles, and pedestrians remains a key challenge. In this paper, we propose DV-DETR, an improved detection model based on the Real-Time Detection Transformer (RT-DETR), specifically optimized for small target detection in high-density scenes. To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. Experimental results on the VisDrone2019 dataset show that DV-DETR achieves an mAP@0.5 of 50.1%, a 1.7% improvement over the baseline model, while increasing detection speed from 75 FPS to 90 FPS, meeting real-time processing requirements. These improvements not only enhance the model’s accuracy and efficiency but also provide practical significance in complex, high-density urban environments, supporting real-world applications in UAV-based surveillance and monitoring tasks. |
| format | Article |
| id | doaj-art-09cf52d87e404578a7d9cb44a447a412 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-09cf52d87e404578a7d9cb44a447a4122024-11-26T18:21:46ZengMDPI AGSensors1424-82202024-11-012422737610.3390/s24227376DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETRXiaolong Wei0Ling Yin1Liangliang Zhang2Fei Wu3School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaFor drone-based detection tasks, accurately identifying small-scale targets like people, bicycles, and pedestrians remains a key challenge. In this paper, we propose DV-DETR, an improved detection model based on the Real-Time Detection Transformer (RT-DETR), specifically optimized for small target detection in high-density scenes. To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. Experimental results on the VisDrone2019 dataset show that DV-DETR achieves an mAP@0.5 of 50.1%, a 1.7% improvement over the baseline model, while increasing detection speed from 75 FPS to 90 FPS, meeting real-time processing requirements. These improvements not only enhance the model’s accuracy and efficiency but also provide practical significance in complex, high-density urban environments, supporting real-world applications in UAV-based surveillance and monitoring tasks.https://www.mdpi.com/1424-8220/24/22/7376transformersmall target detectionreal-time taskRT-DETR algorithm |
| spellingShingle | Xiaolong Wei Ling Yin Liangliang Zhang Fei Wu DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR Sensors transformer small target detection real-time task RT-DETR algorithm |
| title | DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR |
| title_full | DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR |
| title_fullStr | DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR |
| title_full_unstemmed | DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR |
| title_short | DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR |
| title_sort | dv detr improved uav aerial small target detection algorithm based on rt detr |
| topic | transformer small target detection real-time task RT-DETR algorithm |
| url | https://www.mdpi.com/1424-8220/24/22/7376 |
| work_keys_str_mv | AT xiaolongwei dvdetrimproveduavaerialsmalltargetdetectionalgorithmbasedonrtdetr AT lingyin dvdetrimproveduavaerialsmalltargetdetectionalgorithmbasedonrtdetr AT liangliangzhang dvdetrimproveduavaerialsmalltargetdetectionalgorithmbasedonrtdetr AT feiwu dvdetrimproveduavaerialsmalltargetdetectionalgorithmbasedonrtdetr |