In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.
Our previous research REF proposed a cascade detector that detects the road sign corners and combines them into a road sign.
FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Engineering Computer Science Medicine