# Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

arXiv: https://arxiv.org/abs/1903.06391 · HTML: https://mlsanity.com/paper/1903.06391/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection](https://mlsanity.com/paper/2208.13930/) (2022)
- [Out-of-Distribution Detection for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2209.14435/) (2022)
- [Hyperdimensional Feature Fusion for Out-Of-Distribution Detection](https://mlsanity.com/paper/2112.05341/) (2021)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023)
- [What's in the Black Box? The False Negative Mechanisms Inside Object Detectors](https://mlsanity.com/paper/2203.07662/) (2022)
- [Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems](https://mlsanity.com/paper/2303.01300/) (2023)
- [Optimizing Delegation in Collaborative Human-AI Hybrid Teams](https://mlsanity.com/paper/2402.05605/) (2024)
- [Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns](https://mlsanity.com/paper/2404.07685/) (2024)
- [Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection](https://mlsanity.com/paper/2404.17427/) (2024)
- [Safety Monitoring of Machine Learning Perception Functions: a Survey](https://mlsanity.com/paper/2412.06869/) (2024)
- [Enhancing Your Trained DETRs with Box Refinement](https://mlsanity.com/paper/2307.11828/) (2023)
- [Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework](https://mlsanity.com/paper/2307.04533/) (2023)
- [Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing](https://mlsanity.com/paper/2604.26411/) (2026)

## Abstract (arXiv)

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.