A lightweight real-time DETR for high-precision object detection in intelligent driving scenarios

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Precise perception of road conditions and long-distance traffic signs constitutes the core prerequisite for reliable decision-making in autonomous driving systems, while complex background interference remains a critical bottleneck for such detection tasks. To address this issue, this paper proposes a lightweight detection model (CS-DETR) based on Real-Time Detection Transformer (RT-DETR). Specifically, we first design a channel-separable gated progressive deformable convolution (CS-GPDC) that combines lightweight characteristics with receptive field enhancement capabilities. Second, we construct a lightweight backbone network (CSLIT) centered on this convolution, integrating multi-head attention (MHA) and ConvBN techniques. Finally, we introduce a resolution-aware position fusion module (RAP-FM), which enables efficient cross-scale feature interaction and fusion through adaptive weight adjustment, thereby further enhancing feature representation capabilities. Experimental results demonstrate that the CS-DETR model achieves a mean average precision (mAP) of 96.1% on the KITTI dataset, representing a 4.4 percentage point improvement over the RT-DETR model. On the GTSDB dataset, it attains an mAP of 97.3%, delivering a 2.6 percentage point enhancement relative to RT-DETR. Meanwhile, the model reduces its parameter count by 12.9 million, achieving a dual optimization of detection accuracy and lightweight performance while ensuring real-time detection capabilities. Ablation experiments validate the effectiveness of the proposed feature extraction and cross-scale fusion modules in boosting detection accuracy.