6 research outputs found
LaneMapper: A City-scale Lane Map Generator for Autonomous Driving
Autonomous vehicles require lane maps to help navigate from a start to a goal position in a safe, comfortable and quick manner. A lane map represents a set of features inherent to the road, such as lanes, stop signs, traffic lights, and intersections. We present a novel approach to detect multiple lane boundaries and traffic signs to create a 3D city-scale map of the driving environment. We detect, recognize and track lane boundaries with multimodal sensory and prior inputs, such as camera, LiDAR, and GPS/IMU, to assist autonomous driving. We detect and classify traffic signs from the image considering high reflectivity of LiDAR points and further register the locations of traffic signs and lane boundaries together in the world coordinate frame. We have also made our code base open-source for the research community to tweak or use our algorithm for their purposes
LaneMapper: A City-scale Lane Map Generator for Autonomous Driving
Autonomous vehicles require lane maps to help navigate from a start to a goal position in a safe, comfortable and quick manner. A lane map represents a set of features inherent to the road, such as lanes, stop signs, traffic lights, and intersections. We present a novel approach to detect multiple lane boundaries and traffic signs to create a 3D city-scale map of the driving environment. We detect, recognize and track lane boundaries with multimodal sensory and prior inputs, such as camera, LiDAR, and GPS/IMU, to assist autonomous driving. We detect and classify traffic signs from the image considering high reflectivity of LiDAR points and further register the locations of traffic signs and lane boundaries together in the world coordinate frame. We have also made our code base open-source for the research community to tweak or use our algorithm for their purposes
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CCTSDB 2021: A more comprehensive traffic sign detection benchmark
Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents an efficient and layout-independent Automatic License
Plate Recognition (ALPR) system based on the state-of-the-art YOLO object
detector that contains a unified approach for license plate (LP) detection and
layout classification to improve the recognition results using post-processing
rules. The system is conceived by evaluating and optimizing different models,
aiming at achieving the best speed/accuracy trade-off at each stage. The
networks are trained using images from several datasets, with the addition of
various data augmentation techniques, so that they are robust under different
conditions. The proposed system achieved an average end-to-end recognition rate
of 96.9% across eight public datasets (from five different regions) used in the
experiments, outperforming both previous works and commercial systems in the
ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other
datasets, the proposed approach achieved competitive results to those attained
by the baselines. Our system also achieved impressive frames per second (FPS)
rates on a high-end GPU, being able to perform in real time even when there are
four vehicles in the scene. An additional contribution is that we manually
labeled 38,351 bounding boxes on 6,239 images from public datasets and made the
annotations publicly available to the research community