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%).
By Sepideh Gohari, Goodarz Mehr, Azim Eskandarian
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...
arXiv:2602. 07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications.
By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy
arXiv:2605.22455v2 Announce Type: replace-cross
Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...
By Valeria Pais, Malena Mendilaharzu, Daniele Faccio, Luis Oala, Christoph Clausen, Bruno Sanguinetti
ShadowCast is a novel on-the-fly, context-aware shadow augmentation method for remote sensing image segmentation. It synthesizes shadows by extracting masks of elevated objects from segmentation labels and projecting synthetic shadows along an inferred cast-shadow direction, without requiring additional networks or metadata. Experiments on Vaihingen, Potsdam, and LoveDA demonstrate consistent performance gains across various state-of-the-art segmentation architectures and when combined with standard augmentation and regularization techniques.
By Yechan Kim, Dongho Yoon, Jongmin Joo, JongHyun Park, Moongu Jeon
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.
By Jonel Roman, Ryan Sirjue, Peter Nguyen, Daniel Krutky, Juan Jesus, Sudip Dhakal
arXiv:2512.08912v2 Announce Type: replace
Abstract: Nighttime environments pose significant challenges for camera-based perception, as existing methods passively rely on the scene lighting. We introd...
By Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi, Fabien Moutarde
arXiv:2610.00141v1 Announce Type: new
Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...
By Gur Levy Birkental, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
The paper presents a method for creating LiDAR-based 3D object detectors that remain accurate under point‑cloud corruptions while meeting strict latency and memory constraints. It achieves robustness by generating corrupted samples that mimic extreme weather and training detectors to focus on hard samples, guided by perturbation sensitivity estimated from an ensemble of seven heterogeneous detectors. Efficiency is improved by narrowing the network and using offline neighborhood‑density measurements to reduce the number of points processed at each sampling stage, resulting in significant gains in AP under corruption and reductions in inference latency and GPU memory usage across multiple datasets and detector architectures.
By Tingxu Han, Zijie Shen, An Guo, Lyuke Wang, Chunrong Fang, Zhenyu Chen, Wei Song
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.
By Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez, Adel N. Toosi
The paper introduces a depth‑aware pothole detection framework that fuses RGB‑D sensor data and evaluates five architectures—YOLOv8n, YOLOv8nSeg, YOLOv9t, RTDETRL, and RTDETRX—on the PothRGBD dataset. YOLOv8nSeg achieves the highest detection performance (mAP@50 = 0.9556, mAP@50_95 = 0.6758) and the most accurate depth estimate (2.96 cm), while YOLOv8n offers the fastest inference (3.6 ms) and RTDETRX delivers the highest detection confidence (92.70 %). The study also shows that even after RANSAC orthorectification, bounding‑box models overestimate pothole depth by 0.16–0.21 cm, indicating a structural bias rather than a calibration error.
By Md Monjurul Ahsan Prodhan, Md Nour Hossain
DriveOSS is a new real‑world multimodal dataset and benchmark for 3D anomaly segmentation and open‑set semantic segmentation in autonomous driving. It contains 12,661 synchronized frames from six RGB cameras, a 128‑beam LiDAR, and GPS/IMU, covering 52 driving scenes under various weather conditions. The data are split into in‑distribution (ID) scenes for training and out‑of‑distribution (OOD) scenes for validation and testing, with unknown objects labeled as an outlier class to challenge current 3D perception systems.
By Zhiran Yan, Savankumar Bhanderi, Sourabh Lolge, Rui Song, Girdharbhai Paneliya, Shiva Agrawal, Gordon Elger