In autonomous driving scenarios，pedestrian detection performance can degrade substantially because of dramatic variations in pedestrian scale，frequent occlusions，and interference caused by complex backgrounds. To overcome these difficulties，we introduce a novel real-time pedestrian detection algorithm called frequency-enhanced and boundary-aware detection Transformer （FEBA-DETR），which integrates multiple frequency-domain enhancement strategies with a boundary-aware mechanism. By leveraging these complementary components，the proposed method achieves reliable pedestrian detection under the aforementioned challenging conditions.

Real-time detection Transformer （RT-DETR） has demonstrated promising object detection performance，but it still faces challenges in capturing fine-grained details of objects at multiple scales，particularly for small objects，and in effectively leveraging frequency-domain information. To address these limitations，we propose an enhanced RT-DETR architecture that integrates several innovative modules and techniques. For improved backbone efficiency and feature propagation，our model incorporates cross-stage partial connections，which strengthen gradient flow across network stages and reduce redundant computations without sacrificing representation power. Building on this foundation，we introduce a frequency-aware module that explicitly harnesses high- and low-frequency visual cues to enrich the feature representations，ensuring that important textural and edge details are preserved and emphasized throughout the network. At the feature interaction level，we design a frequency-aware attention-based intra-scale feature interaction （FAIFI） mechanism. By enabling features at the same scale to communicate through attention operations modulated by frequency-domain signals，FAIFI allows the detector to capture subtle object characteristics that might otherwise be missed in single-scale processing. Moreover，we employ a boundary-aware feature aggregation （BAFA） strategy to improve multiscale feature fusion. BAFA focuses on preserving object boundary information when combining features from different resolution levels，thus helping maintain precise localization cues and contributing to accurate detection of objects with distinct edges. In addition to these architectural improvements，we refine the training process with an Inner-GIoU loss，a modified bounding box regression loss that places high emphasis on the overlapping area between predicted and ground-truth boxes. This tailored loss function drives the model to achieve tight and highly reliable localization. Furthermore，to bolster the model’s robustness and expand the diversity of training data，we introduce a novel frequency-based data augmentation approach that utilizes 2D discrete wavelet transform. By decomposing and altering the frequency components of training images，this augmentation method exposes the network to a broad variety of spatial-frequency patterns and helps prevent overfitting to common textures. Together，these enhancements considerably boost the detection accuracy and reliability of RT-DETR. Experiments on challenging object detection benchmarks indicate that the improved RT-DETR achieves higher average precision than its baseline counterpart，especially on small or texture-rich objects，and maintains real-time inference speed. Our results underscore the benefit of incorporating frequency-domain insights and boundary-aware feature processing into the RT-DETR framework for high-performance object detection.

We rigorously evaluate our approach on three widely used person-detection benchmarks： CityPersons，WiderPersons，and ACDC. On CityPersons，our FEBA-DETR model achieves notable improvements over the baseline RT-DETR，with AP50 increasing by 2.3% and AP50：95 increasing by 2.0%； incorporating our data augmentation technique boosts performance，raising the aforementioned gains to 3% and 2.4%，respectively. On the occlusion-specific subsets of CityPersons，the miss rate decreases by 4.92% for the partially occluded “Reasonable” category and by 2.82% for the heavily occluded “Heavy” category. Similarly，on the challenging WiderPersons dataset，FEBA-DETR again surpasses the baseline，with AP50 and AP50：95 improvements of 1.1% and 0.7%，respectively. These consistent gains underscore the robust generalization of our method across diverse datasets. Moreover，on the ACDC dataset，which specifically assesses robustness in complex environmental scenarios，FEBA-DETR boosts AP50 and AP50：95 by 1.9% and 1.3%，respectively，with even greater improvements of 3.1% and 2.4%，respectively，after applying our data augmentation strategy. These extensive experimental results convincingly affirm the effectiveness of our multilevel frequency enhancement and boundary-awareness techniques，substantially advancing pedestrian detection in challenging autonomous driving contexts.

Experimental evaluations show that FEBA-DETR delivers notable advancements in detecting pedestrians，particularly those who are either small in scale or partially occluded. The model also maintains consistent performance under highly challenging and dynamic conditions. These findings underscore the practical advantages of incorporating frequency-domain enhancements and boundary-awareness strategies. Together，these design choices contribute to a dependable，accurate pedestrian detection system，which is especially valuable for autonomous driving applications where safety and precision are critical.