Traffic Light Detection using Convolutional Neural Networks and Lidar Data
| Autor: | Sheng-Wei Chan, Tien-Wen Yeh, Che-Tsung Lin, Huei-Yung Lin, Ssu-Yun Lin, Yan-Yu Lin |
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| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Computer science business.industry 05 social sciences Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Urban road Convolutional neural network Object detection Traffic signal 020204 information systems 0502 economics and business 0202 electrical engineering electronic engineering information engineering Focal length Lidar data Artificial intelligence business Classifier (UML) |
| Zdroj: | ISPACS |
| DOI: | 10.1109/ispacs48206.2019.8986310 |
| Popis: | This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition. |
| Databáze: | OpenAIRE |
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