Vehicle detection method based on multi-layer selective feature for UAV aerial images
Abstract (UAVs) play a critical role in traffic flow monitoring and rapid accident response. However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. To address these issues, a lightweight vehicle...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00150-y |
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| author | Yinbao Ma Yuyu Meng Jiuyuan Huo |
| author_facet | Yinbao Ma Yuyu Meng Jiuyuan Huo |
| author_sort | Yinbao Ma |
| collection | DOAJ |
| description | Abstract (UAVs) play a critical role in traffic flow monitoring and rapid accident response. However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. To address these issues, a lightweight vehicle detection model is developed based on UAV aerial imagery. In the backbone, a Receptive-Field Attention Convolution (RFAConv) module is introduced to retain detailed features during the downsampling process. To handle complex environments such as illumination changes and dense occlusion, a Cross-Stage Partial Fusion Bi-Level Routing Attention Network (CSP-BLRAN) module is employed to enhance contextual feature representation and suppress false and missed detections. In the neck, a multi-layer selective feature fusion pyramid (MS-FPN) is constructed to perform attention-based filtering on high-level semantic features and low-level spatial details, followed by feature enhancement via multiplication and global semantic refinement through residual connections. For the detection head, a Generalized Wasserstein Distance Loss (GWDLoss) function is proposed to quantify positional and scale discrepancies, improving the adaptability of bounding box regression to geometric variations. Extensive experiments on the VisDrone and Vehicle datasets demonstrate that the proposed method reduces the number of parameters by 22.9% and achieves mAP@50–95 improvements of 4.5% and 7.3%, respectively, over YOLO11n. The approach also surpasses mainstream models such as YOLO11s with significantly fewer parameters, confirming its effectiveness and practical value in resource-constrained low-altitude UAV-based detection tasks. |
| format | Article |
| id | doaj-art-9151c1bdabcc494ba84416b3a30ed5d9 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-9151c1bdabcc494ba84416b3a30ed5d92025-08-20T03:43:31ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-07-0137612810.1007/s44443-025-00150-yVehicle detection method based on multi-layer selective feature for UAV aerial imagesYinbao Ma0Yuyu Meng1Jiuyuan Huo2School of Electronic and Information Engineering, Lanzhou Jiaotong UniversitySchool of Electronic and Information Engineering, Lanzhou Jiaotong UniversitySchool of Electronic and Information Engineering, Lanzhou Jiaotong UniversityAbstract (UAVs) play a critical role in traffic flow monitoring and rapid accident response. However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. To address these issues, a lightweight vehicle detection model is developed based on UAV aerial imagery. In the backbone, a Receptive-Field Attention Convolution (RFAConv) module is introduced to retain detailed features during the downsampling process. To handle complex environments such as illumination changes and dense occlusion, a Cross-Stage Partial Fusion Bi-Level Routing Attention Network (CSP-BLRAN) module is employed to enhance contextual feature representation and suppress false and missed detections. In the neck, a multi-layer selective feature fusion pyramid (MS-FPN) is constructed to perform attention-based filtering on high-level semantic features and low-level spatial details, followed by feature enhancement via multiplication and global semantic refinement through residual connections. For the detection head, a Generalized Wasserstein Distance Loss (GWDLoss) function is proposed to quantify positional and scale discrepancies, improving the adaptability of bounding box regression to geometric variations. Extensive experiments on the VisDrone and Vehicle datasets demonstrate that the proposed method reduces the number of parameters by 22.9% and achieves mAP@50–95 improvements of 4.5% and 7.3%, respectively, over YOLO11n. The approach also surpasses mainstream models such as YOLO11s with significantly fewer parameters, confirming its effectiveness and practical value in resource-constrained low-altitude UAV-based detection tasks.https://doi.org/10.1007/s44443-025-00150-yMulti-scaleUAV aerial image vehicle detectionDownsamplingFeature fusionLoss function |
| spellingShingle | Yinbao Ma Yuyu Meng Jiuyuan Huo Vehicle detection method based on multi-layer selective feature for UAV aerial images Journal of King Saud University: Computer and Information Sciences Multi-scale UAV aerial image vehicle detection Downsampling Feature fusion Loss function |
| title | Vehicle detection method based on multi-layer selective feature for UAV aerial images |
| title_full | Vehicle detection method based on multi-layer selective feature for UAV aerial images |
| title_fullStr | Vehicle detection method based on multi-layer selective feature for UAV aerial images |
| title_full_unstemmed | Vehicle detection method based on multi-layer selective feature for UAV aerial images |
| title_short | Vehicle detection method based on multi-layer selective feature for UAV aerial images |
| title_sort | vehicle detection method based on multi layer selective feature for uav aerial images |
| topic | Multi-scale UAV aerial image vehicle detection Downsampling Feature fusion Loss function |
| url | https://doi.org/10.1007/s44443-025-00150-y |
| work_keys_str_mv | AT yinbaoma vehicledetectionmethodbasedonmultilayerselectivefeatureforuavaerialimages AT yuyumeng vehicledetectionmethodbasedonmultilayerselectivefeatureforuavaerialimages AT jiuyuanhuo vehicledetectionmethodbasedonmultilayerselectivefeatureforuavaerialimages |