Traffic Light and Arrow Signal Recognition Based on a Unified Network
We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length camera...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/17/8066 |
| _version_ | 1797521690511540224 |
|---|---|
| author | Tien-Wen Yeh Huei-Yung Lin Chin-Chen Chang |
| author_facet | Tien-Wen Yeh Huei-Yung Lin Chin-Chen Chang |
| author_sort | Tien-Wen Yeh |
| collection | DOAJ |
| description | We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition. |
| first_indexed | 2024-03-10T08:16:07Z |
| format | Article |
| id | doaj.art-e913c09e891e4d47b1e2239086d0ee62 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:16:07Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e913c09e891e4d47b1e2239086d0ee622023-11-22T10:20:58ZengMDPI AGApplied Sciences2076-34172021-08-011117806610.3390/app11178066Traffic Light and Arrow Signal Recognition Based on a Unified NetworkTien-Wen Yeh0Huei-Yung Lin1Chin-Chen Chang2Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi 621, TaiwanDepartment of Computer Science and Information Engineering, National United University, Miaoli 360, TaiwanWe present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition.https://www.mdpi.com/2076-3417/11/17/8066autonomous vehiclecomputer visiontraffic light recognitionconvolutional neural networks |
| spellingShingle | Tien-Wen Yeh Huei-Yung Lin Chin-Chen Chang Traffic Light and Arrow Signal Recognition Based on a Unified Network Applied Sciences autonomous vehicle computer vision traffic light recognition convolutional neural networks |
| title | Traffic Light and Arrow Signal Recognition Based on a Unified Network |
| title_full | Traffic Light and Arrow Signal Recognition Based on a Unified Network |
| title_fullStr | Traffic Light and Arrow Signal Recognition Based on a Unified Network |
| title_full_unstemmed | Traffic Light and Arrow Signal Recognition Based on a Unified Network |
| title_short | Traffic Light and Arrow Signal Recognition Based on a Unified Network |
| title_sort | traffic light and arrow signal recognition based on a unified network |
| topic | autonomous vehicle computer vision traffic light recognition convolutional neural networks |
| url | https://www.mdpi.com/2076-3417/11/17/8066 |
| work_keys_str_mv | AT tienwenyeh trafficlightandarrowsignalrecognitionbasedonaunifiednetwork AT hueiyunglin trafficlightandarrowsignalrecognitionbasedonaunifiednetwork AT chinchenchang trafficlightandarrowsignalrecognitionbasedonaunifiednetwork |