Knowledge-Guided Failure Prediction: Detecting When Object Detectors Miss Safety-Critical Objects

2026-03-26

AI Summary

This work addresses the critical challenge of silent failures in object detectors—such as missed pedestrian detections in safety-critical scenarios—that evade conventional out-of-distribution (OOD) detection methods. To this end, the authors propose KGFP, a knowledge-guided failure prediction framework that formulates detector failures as semantic inconsistencies between the detector’s internal features and embeddings from a vision foundation model. Leveraging a dual-encoder architecture and angular distance metrics, KGFP establishes a runtime selective prediction gating mechanism. Evaluated on pedestrian detection within the COCO benchmark, KGFP improves recall from 64.3% to 84.5% at a 5% false positive rate and consistently outperforms existing OOD detection approaches across six COCO-O domains.

Technology Category

• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Object detectors deployed in safety-critical environments can fail silently, e.g. missing pedestrians, workers, or other safety-critical objects without emitting any warning. Traditional Out Of Distribution (OOD) detection methods focus on identifying unfamiliar inputs, but do not directly predict functional failures of the detector itself. We introduce Knowledge Guided Failure Prediction (KGFP), a representation-based monitoring framework that treats missed safety-critical detections as anomalies to be detected at runtime. KGFP measures semantic misalignment between internal object detector features and visual foundation model embeddings using a dual-encoder architecture with an angular distance metric. A key property is that when either the detector is operating outside its competence or the visual foundation model itself encounters novel inputs, the two embeddings diverge, producing a high-angle signal that reliably flags unsafe images. We compare our novel KGFS method to baseline OOD detection methods. On COCO person detection, applying KGFP as a selective-prediction gate raises person recall among accepted images from 64.3% to 84.5% at 5% False Positive Rate (FPR), and maintains strong performance across six COCO-O visual domains, outperforming OOD baselines by large margins. Our code, models, and features are published at https://gitlab.cc-asp.fraunhofer.de/iosb_public/KGFP.

Problem

• failure prediction
• object detection
• safety-critical
• out-of-distribution detection
• missed detection

Innovation

• Knowledge-Guided Failure Prediction
• Object Detection Failure
• Semantic Misalignment
• Visual Foundation Model
• Selective Prediction