HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles - Xiaona Song; Bin Fan; Haichao Liu; Lijun Wang; Jinxing Niu (Sensors)
Object detection is essential for the perception systems of intelligent driving vehicles. RT-DETR has emerged as a prominent model. However, its direct application in intelligent driving vehicles still faces issues with the misdetection of occluded or small targets. To address these challenges, we propose a High-Precision Real-Time object detection algorithm (HPRT-DETR). We designed a Basic-iRMB-CGA (BIC) Block for a backbone network that efficiently extracts features and reduces the model’s parameters. We thus propose a Deformable Attention-based Intra-scale Feature Interaction (DAIFI) module by combining the Deformable Attention mechanism with the Intra-Scale Feature Interaction module. This enables the model to capture rich semantic features and enhance object detection accuracy in occlusion. The Local Feature Extraction Fusion (LFEF) block was created by integrating the local feature extraction module with the CNN-based Cross-scale Feature Fusion (CCFF) module. This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. Experiments on the KITTI dataset show that, compared to RT-DETR, HPRT-DETR improves mAP50 and FPS by 1.98% and 15.25%, respectively. Additionally, its generalization ability is assessed on the SODA 10M dataset, where HPRT-DETR outperforms RT-DETR in most evaluation metrics, confirming the model’s effectiveness.