A Comparative Study between State-of-the-Art Object Detectors for Traffic Light Detection

Gokul, R.; Nirmal, A. J.; Bharath, K R; Pranesh, MP; Karthika, R.

doi:10.1109/ic-etite47903.2020.449

Cited by 23 publications

(5 citation statements)

References 26 publications

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“…This sharing of layers enhances the overall efficiency of the network. The Faster RCNN model has emerged as one of the state-of-the-art object detectors, surpassing the performance of other traditional models such as YOLO, SSD, and other traditional models on several key metrics [26,27].…”

Section: Computer Vision Frameworkmentioning

confidence: 99%

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

et al. 2023

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Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.

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Section: Computer Vision Frameworkmentioning

confidence: 99%

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

et al. 2023

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“…The dataset used were microsoft common objects in context (MS COCO) [30] and street view house numbers dataset (SVHN) [31] with the acquired result of 82.2% precision and 82.78% recall. Based on the review of past research, it is clear that multiple researchers had worked on traffic light detection and recognition systems [32] and compared multiple algorithms to decide the best method for traffic light detection and classification [33]. Nevertheless, this does not happen to the detection and classification of the timer counter on the traffic light.…”

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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“…Their results show that Faster R-CNN outperformed R-FCN [79] and SSD in terms of mAP. Later, Gokul et al [51] have also demonstrated that Faster-R-CNN has the best trade-off between accuracy and speed compared to YOLOv2 and YOLOv3.…”

Section: A Modifications Of Generic Object Detectorsmentioning

confidence: 99%

German Traffic Sign Recognition Using Convolutional Neural Network

Santosh¹,

Kumar²,

Sandeep³

et al. 2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rulebased and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.

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A Comparative Study between State-of-the-Art Object Detectors for Traffic Light Detection

Cited by 23 publications

References 26 publications

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

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

German Traffic Sign Recognition Using Convolutional Neural Network

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