computer-vision-6

Traffic Sign Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in traffic-sign-detection-6

Trend

Dataset

Best Model

Actions

GTSDB

CCTSDB2021

TT100K

Libraries

i

Use these libraries to find traffic-sign-detection-6 models and implementations

Datasets

CURE-TSR

CVL Traffic Signs Dataset

CURE-TSD

CCTSDB2021

TT100K

Subtasks

No subtasks available.

Most implemented papers

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

Amara Dinesh Kumar•Thu May 10 2018

A novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset and provides resistance to the spatial variances.

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A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

Steven L. Waslander, Ali Harakeh, Alex D. Pon, Oles Andrienko•Mon May 07 2018

This work presents a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets to solve the overlapping issue where instances from one dataset are not labelled in the other dataset.

45 0

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Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions

Dogancan Temel, Min-Hung Chen, G. Al-Regib, Tariq A. Alshawi•Mon Feb 18 2019

A detailed description of the CURE-TSD dataset is provided, the characteristics of the top performing algorithms are analyzed, and the robustness of the benchmarked algorithms with respect to sign size, challenge type and severity is investigated.

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Model Agnostic Defence Against Backdoor Attacks in Machine Learning

Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, Sudipta Chattopadhyay•Mon Aug 05 2019

This article presents Neo, a model agnostic framework to detect and mitigate backdoor attacks in image classification ML models, and reveals that despite being a blackbox approach, Neo is more effective in thwarting backdoor attacks than the existing techniques.

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Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

Dogancan Temel, Min-Hung Chen, G. Al-Regib•Wed Aug 28 2019

The CURE-TSD-Real dataset is introduced, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems and shows that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance.

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Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]

Dogancan Temel, G. Al-Regib•Wed Feb 28 2018

An overview of the VIP Cup 2017 experience including competition setup, teams, technical approaches, participation statistics, and competition experience through finalist teams members' and organizers' eyes is shared.

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Evaluation of deep neural networks for traffic sign detection systems

Luis Miguel Soria-Morillo, Á. A. García, Juan Antonio Álvarez-García•Wed Oct 31 2018

This paper analyses the state-of-the-art of several object-detection systems combined with various feature extractors previously developed by their corresponding authors, finding that Faster R-CNN Inception Resnet V2 obtains the best mAP, while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time.

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Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Domen Tabernik, D. Skočaj•Sun Mar 31 2019

A convolutional neural network approach, the mask R-CNN, is adopted to address the full pipeline of detection and recognition with automatic end-to-end learning, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

285 0

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Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

N. Sebe, Rodrigo Berriel, A. D. Souza, Thiago Oliveira-Santos, Lucas Tabelini, T. M. Paixão, C. Badue•Sun Jun 30 2019

A novel database generation method that requires only arbitrary natural images, which is able to detect traffic signs with an average precision, recall, and F1-score of about 94%, 91% and 93%, respectively, and the experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background.

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A real-time and high-precision method for small traffic-signs recognition

Zhihan Lv, Junzhou Chen, Kunkun Jia, Wenquan Chen, Rong-hui Zhang•Fri Sep 24 2021

This article proposes an efficient method for small-size traffic-signs recognition, named traffic-Signs recognition small-aware, with the inspiration of the state-of-the-art object detection framework YOLOv4 and Y OLOv5, and proposes a data augmentation method named Random Erasing-Attention, which can increase difficult samples and enhance the robustness of the model.

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