Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 46 references
Computer Science
TL;DR
This work investigated intrinsic failures and anomalies in semantic segmentation and proposed a unified framework that manages these errors specifically for autonomous vehicles, and developed an anomaly fusion module that integrates data from both inlier and void masks, alongside pyramid features from the backbone, ultimately delivering impressive anomaly detection capabilities.
Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems that depend on scene parsing in the perception stage. Many recent methods address this by using self-supervised foundation models to train density estimators that yield l...
Anja Delic, Jurica Runtas, Marin Orsic et al.· 0 citations
Semantic segmentation algorithms constitute a critical component for vision-based perception in robotics and autonomous driving. Real-world deployment in uncertain environments requires such algorithms to be both low-latency and robust against common corruptions. However, the limited representation ability of real-time...
Deep learning has significantly improved the performance of semantic segmentation in high-resolution imagery and computer vision tasks. However, standard segmentation models treat all pixel-wise predictions equally, despite the fact that predictions close to the decision boundary are inherently uncertain. This limitati...
Antoni Mestre, Franccesco Malafarina· Engineering Science & Te...· 0 citations
A novel, end to end processing pipeline that simultaneously tackles dataset size and small object detection bottlenecks is introduced and improves the detection of distant and heavily compressed traffic signs.
U. Vijayalakshmi, M. Babu· Scientific Reports· 0 citations
Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo. The reliable detection and segmentation of these out-of-distribution (OOD) objects is therefore crucial for a safe understanding of the environment and decision-making....
Serin Varghese, Fabian Hüger, Kira Maag· 0 citations
Static images and vehicle-mounted video are the two primary data sources for road inspection. Since cracks, potholes, and patched areas can all be considered anomalies on the road surface, Contrastive Language–Image Pre-training (CLIP)-based anomaly segmentation provides a promising approach under limited labeled data....
Si-Yuan He, Yuchun Huang, Chen Wang et al.· Remote Sensing· 0 citations
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