A Real-time Vehicle Detection Pipeline with Data-centric Enhancements and Multi-stage DETR Distillation

Huy Minh Nhat Nguyen, Hieu Dinh Trung Pham, Khang Minh Le, Cuong Tuan Nguyen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 5441-5448

Real-time vehicle detection often requires trading off accuracy for speed. To validate a solution that excels on both fronts, we adopt fisheye imagery, a domain where extreme radial distortion and scale variation defeat standard detectors, as a rigorous testbed. Our pipeline comprises three key stages: (1) Multi-stage DETR Distillation, a four-phase knowledge transfer leveraging KD-DETR's fixed distillation queries with separate head- and feature-level stages to avoid gradient conflicts and ensure progressive learning; (2) Data-centric Enhancements, creating a diverse training pool via Co-DETR pseudo-labeling, CycleGAN-Turbo day-to-night style transfer, and object-level flash/blur augmentations; and (3) Adaptive Sample Mining, which dynamically upsamples complex examples to sharpen the model's focus. When paired with D-FINE-M, our method achieves an F1 score of 0.6318 at 145 FPS on the AI City Challenge 2024 Track 4 test set, and with D-FINE-N, it reaches 781 FPS with an F1-score of 0.5597, all measured on an RTX 4090. Evaluated on the challenging FishEye8K benchmark, our approach delivers strong accuracy while maintaining real-time FPS. By ignoring fisheye distortions and treating them as a domain-agnostic stress test, we demonstrate that this data-centric, multi-stage distillation framework generalizes seamlessly to standard vehicle and broader object detection tasks, offering a unified solution for high-precision, real-time vision systems.