Real Time Traffic Light Detection and Classification using Deep Learning

Ennahhal, Zakaria; Berrada, Ismaïl; Fardousse, Khalid

doi:10.1109/wincom47513.2019.8942446

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…It was also demonstrated by Jensen et al [12] that using YOLO [13]- [15] with the laboratory for intelligent and safe automobiles (LISA) traffic light dataset [16] and logistic activity recognition challenge (LARa) traffic light dataset [17] had produced 96.38% recall for YOLOv3, 68.06% recall for YOLOv2 and 42.3% recall for YOLOv1. Another research [18] used faster region based convolutional neural networks (R-CNN) [19] and LISA traffic light dataset [16] and Bosch small traffic light dataset [20] and the results achieved were 56.31% mean average precision (mAP) on the Bosch dataset and 76.37% mAP on the LISA dataset.…”

Section: Related Workmentioning

confidence: 99%

Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

Al-Haimi,

Sani,

Izzudin

et al. 2023

IJ-AI

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This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2% and the estimate total confidence rate for red and green are 98.4% and 99.3% respectively. The results were compared with the previous YOLOv5 algorithm, and the results are substantially close to each other as the YOLOv5 accuracy and recall at 97.5% and 97.5% respectively.

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Section: Related Workmentioning

confidence: 99%

Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

Al-Haimi,

Sani,

Izzudin

et al. 2023

IJ-AI

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show abstract

“…Ennahhal et a. [14] evaluate traffic light detection performance on various state-of-the-art object detection models. They find that Faster R-CNN gives the best mean average precision for this task.…”

Section: A Traffic Light Detection and Classification Modelsmentioning

confidence: 99%

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Greer

Isa

Deo

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

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“…Ennahhal et al [5] employed multiple models such as Faster R-CNN, R-FCN and SSD approaches for traffic light detections on 43,007 images from LISA traffic light dataset. They found out that Faster R-CNN yielding the best accuracy of 76.37%, using the evaluation metric of mAP@0.5.…”

Section: Related Workmentioning

confidence: 99%

Traffic Light Recognition Assistance for Colour Vision Deficiency Using Deep Learning

Lee¹,

Ng²,

Yap³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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This research intends to train a model to recognize traffic light signals in real-time to allow a person with Colour Vision Deficiency to identify the current signal of traffic lights with a mobile device's camera. First, LISA Traffic Light Dataset is downloaded obtained from Kaggle. Then, two data pre-processing steps are carried out, namely label map generation and TFRecords conversion. A total of six models,

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Real Time Traffic Light Detection and Classification using Deep Learning

Cited by 11 publications

References 23 publications

Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

Traffic Light Recognition Assistance for Colour Vision Deficiency Using Deep Learning

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