HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention
Special vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feat...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/14/4381 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849251766994993152 |
|---|---|
| author | Kaipeng Wang Guanglin He Xinmin Li |
| author_facet | Kaipeng Wang Guanglin He Xinmin Li |
| author_sort | Kaipeng Wang |
| collection | DOAJ |
| description | Special vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feature DETR), integrating four innovative modules: a Cascaded Spatial Feature Network (CSFNet) backbone with Cross-Efficient Convolutional Gating (CECG) for enhanced long-range detection through hybrid state-space modeling; a Hypergraph-Enhanced Spatial Feature Modulation (HyperSFM) network utilizing hypergraph structures for high-order feature correlations and adaptive multi-scale fusion; a Dual-Domain Feature Encoder (DDFE) combining Bipolar Efficient Attention (BEA) and Frequency-Enhanced Feed-Forward Network (FEFFN) for precise feature weight allocation; and a Spatial-Channel Fusion Upsampling Block (SCFUB) improving feature fidelity through depth-wise separable convolution and channel shift mixing. Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments. |
| format | Article |
| id | doaj-art-56e20376d9af4390aaeddfbc3a50ecdd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-56e20376d9af4390aaeddfbc3a50ecdd2025-08-20T03:56:49ZengMDPI AGSensors1424-82202025-07-012514438110.3390/s25144381HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar AttentionKaipeng Wang0Guanglin He1Xinmin Li2Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, ChinaScience and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, ChinaScience and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, ChinaSpecial vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feature DETR), integrating four innovative modules: a Cascaded Spatial Feature Network (CSFNet) backbone with Cross-Efficient Convolutional Gating (CECG) for enhanced long-range detection through hybrid state-space modeling; a Hypergraph-Enhanced Spatial Feature Modulation (HyperSFM) network utilizing hypergraph structures for high-order feature correlations and adaptive multi-scale fusion; a Dual-Domain Feature Encoder (DDFE) combining Bipolar Efficient Attention (BEA) and Frequency-Enhanced Feed-Forward Network (FEFFN) for precise feature weight allocation; and a Spatial-Channel Fusion Upsampling Block (SCFUB) improving feature fidelity through depth-wise separable convolution and channel shift mixing. Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments.https://www.mdpi.com/1424-8220/25/14/4381special vehicle detectionRT-DETRmulti-scale feature fusiondeep learningobject detection |
| spellingShingle | Kaipeng Wang Guanglin He Xinmin Li HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention Sensors special vehicle detection RT-DETR multi-scale feature fusion deep learning object detection |
| title | HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention |
| title_full | HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention |
| title_fullStr | HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention |
| title_full_unstemmed | HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention |
| title_short | HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention |
| title_sort | hsf detr a special vehicle detection algorithm based on hypergraph spatial features and bipolar attention |
| topic | special vehicle detection RT-DETR multi-scale feature fusion deep learning object detection |
| url | https://www.mdpi.com/1424-8220/25/14/4381 |
| work_keys_str_mv | AT kaipengwang hsfdetraspecialvehicledetectionalgorithmbasedonhypergraphspatialfeaturesandbipolarattention AT guanglinhe hsfdetraspecialvehicledetectionalgorithmbasedonhypergraphspatialfeaturesandbipolarattention AT xinminli hsfdetraspecialvehicledetectionalgorithmbasedonhypergraphspatialfeaturesandbipolarattention |