RL-Net: a rapid and lightweight network for detecting tiny vehicle targets in remote sensing images
Abstract Traffic accidents remain a critical issue that significantly impacts public safety and poses major challenges to intelligent transportation systems. The integration of Unmanned Aerial Vehicles (UAVs) with object detection technology offers a promising solution to this problem. However, exis...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01956-z |
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| Summary: | Abstract Traffic accidents remain a critical issue that significantly impacts public safety and poses major challenges to intelligent transportation systems. The integration of Unmanned Aerial Vehicles (UAVs) with object detection technology offers a promising solution to this problem. However, existing detection networks often exhibit limitations such as missed detections and inadequate real-time performance, particularly for small vehicle targets in remote sensing images, and fail to meet the efficiency requirements of edge computing devices. To address these challenges, this study proposes RL-Net, a rapid and lightweight network model based on enhancements to the YOLOv9s architecture. First, MobileNetV4 is introduced to optimize initial feature extraction, significantly improving the network’s efficiency. Second, the Lightweight Spatial Pyramid Pooling Fast (LSPPF) structure is designed to enhance multiscale feature extraction while accelerating computational speed. Additionally, the Lightweight Representation Cross Stage Partial with ELAN (LRepCSPELAN) module is proposed to further reduce the model’s memory and computational resource demands. Finally, an enhanced feature fusion network is designed to improve detection performance for tiny vehicle targets. Comprehensive evaluations on the VisDrone2019 and UA-DETRAC datasets demonstrate that, compared to YOLOv9s model, RL-Net achieves a 34.5% reduction in parameters, a 4.4% reduction in computations, a 30.8% increase in Frames Per Second (FPS), and improvements of 2.5% in mean Average Precision (mAP) and 2% in recall, effectively balancing detection efficiency and performance. |
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| ISSN: | 2199-4536 2198-6053 |