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

• Published in: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 3748 - 3753
• Main Authors: Rahman, Quazi Marufur, Sunderhauf, Niko, Dayoub, Feras
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• Subjects: Accuracy; Alarm systems; Autonomous vehicles; Benchmark testing; Detectors; Feature extraction; Intelligent robots; Navigation; Object detection; Object recognition
• ISSN: 2153-0866
• DOI: 10.1109/IROS40897.2019.8968525

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic sign as an object plays 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 are proposing 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.