Currently, autonomous driving object detection models face significant data scarcity and generalization challenges when navigating complex Chinese rural traffic scenarios. To address these limitations, we propose a novel real-synthetic mixed object detection dataset tailored specifically for Chinese rural roads and systematically evaluate the performance of 13 mainstream detectors under different real-to-synthetic data ratios, thereby providing empirical evidence for model selection and data strategy design in rural autonomous driving scenarios.
arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type...
The paper surveys one‑stage object detectors for autonomous driving, covering the evolution from early models like YOLOv1 and SSD to recent real‑time architectures such as YOLOv10 and anchor‑free detectors like FCOS and CenterNet. It compares these methods on design choices, feature‑fusion strategies, loss functions, deployment trade‑offs, and benchmark performance, while also summarizing datasets, evaluation metrics, open challenges, and future research directions. The survey emphasizes how one‑stage detectors balance speed, accuracy, efficiency, and robustness, noting the gap between benchmark results and dependable real‑world performance.
The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.
arXiv:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.
The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).
arXiv:2609.01584v1 Announce Type: new Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
arXiv:2409. 16808v3 Announce Type: replace-cross Abstract: Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices.
The paper evaluates out‑of‑the‑box object detection models for automatic target detection and recognition (ATD/R) in military settings. Six YOLO variants and two DETR variants were benchmarked on a new military dataset featuring vehicles, occlusions, and small targets, with performance measured in mAP@0.5 and mAP@0.5:0.95 across air‑to‑ground and ground‑to‑ground perspectives. Findings show larger models and DETR-based approaches perform best, fine‑tuning on the VisDrone dataset improves air‑to‑ground and small‑object performance, yet all models still struggle with small targets in air‑to‑ground scenarios.
Semantically-Guided Domain Randomization (S‑GDR) is an annotation‑free pipeline that uses vision‑language model captioning of a small real reference set, diffusion‑based background synthesis, and mask‑based object composition to generate synthetic training data. In a high‑mix, low‑volume automotive detection benchmark, S‑GDR achieves a mAP50‑95 of 0.739 with only 200 synthetic images, outperforming a domain‑randomized render baseline and several other synthetic data methods under the same budget. These results suggest S‑GDR is a viable alternative for training visual perception systems when annotation, energy, and time resources are severely limited.
arXiv:2505.12254v3 Announce Type: replace-cross Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...