Wang Kaipeng, He Guanglin, Li Xinmin
Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2025 Jul 13;25(14):4381. doi: 10.3390/s25144381.
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.
在智能监控、应急救援和侦察中，特殊车辆检测在复杂环境下的准确性和鲁棒性方面面临重大挑战，这就需要针对关键应用开发先进的检测算法。本文提出了HSF-DETR（超图空间特征DETR），它集成了四个创新模块：一个带有交叉高效卷积门控（CECG）的级联空间特征网络（CSFNet）主干，通过混合状态空间建模增强远距离检测能力；一个利用超图结构进行高阶特征关联和自适应多尺度融合的超图增强空间特征调制（HyperSFM）网络；一个结合双极高效注意力（BEA）和频率增强前馈网络（FEFFN）以实现精确特征权重分配的双域特征编码器（DDFE）；以及一个通过深度可分离卷积和通道移位混合来提高特征保真度的空间通道融合上采样模块（SCFUB）。在一个包含2388张图像的自建特殊车辆数据集上进行的实验表明，HSF-DETR的mAP50和mAP50-95分别达到了96.6%和70.6%，比基线RT-DETR分别提高了3.1%和4.6%，同时计算效率保持在59.7 GFLOPs和18.07 M参数。在VisDrone2019和BDD100K数据集上的跨域验证证实了该方法在不同场景下的泛化能力和鲁棒性，确立了HSF-DETR作为复杂环境下特殊车辆检测的有效解决方案。
