Research Article
Traffic Lights Detection Based on Deep Learning Feature
@INPROCEEDINGS{10.1007/978-3-030-44751-9_32, author={Changhao Wang and GuanWen Zhang and Wei Zhou and Yukun Rao and Yu Lv}, title={Traffic Lights Detection Based on Deep Learning Feature}, proceedings={IoT as a Service. 5th EAI International Conference, IoTaaS 2019, Xi’an, China, November 16-17, 2019, Proceedings}, proceedings_a={IOTAAS}, year={2020}, month={6}, keywords={Traffic lights detection Deep learning Region proposal Classification}, doi={10.1007/978-3-030-44751-9_32} }- Changhao Wang
GuanWen Zhang
Wei Zhou
Yukun Rao
Yu Lv
Year: 2020
Traffic Lights Detection Based on Deep Learning Feature
IOTAAS
Springer
DOI: 10.1007/978-3-030-44751-9_32
Abstract
Traffic lights detection is an important task for intelligent vehicles. It is non-trivial due to variance backgrounds and illumination conditions. Therefore, a traffic lights detection system that can apply to different scenes is necessary. In this paper, we research the traffic lights detection based on deep learning, which can extract features with representation and robustness from input image automatically and avoid using artificial features. The approach of traffic lights detection proposed in this paper includes two stages: (1) region proposal and (2) classification of traffic lights. Firstly, we propose a region proposal method based on intensity, color, and geometric information of traffic lights. Secondly, convolutional neural network (CNN) was introduced for the traffic lights classification, obtaining 99.6% average accuracy. For detection, we evaluate our system on 6804 images of different scenes, the recall and accuracy of detection achieve 99.2% and 98.5% respectively.
