Pakistani traffic-sign recognition using transfer learning

Nadeem, Zain; Khan, Zainullah; Mir, Usama; Mir, Umer Iftikhar; Khan, Shahnawaz; Nadeem, Hamza; Sultan, Junaid

doi:10.1007/s11042-022-12177-8

Cited by 9 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kundo et al [ 19 ] proposes a bagging ensemble of three transfer learning models, InceptionV3, ResNet34 and DenseNet201, that outperformed the state of the art methods by 1.56%. Nadeem et al [ 26 ] uses transfer learning for Pakistani traffic-sign recognition. They use a model trained on the German traffic-sign recognition, and with additional pre-processing and regularisation, they achieved competitive results on a small available dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Texture classification for visual data using transfer learning

Goyal

Sharma

2022

Multimed Tools Appl

View full text Add to dashboard Cite

The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because of textures. This work aims to offer texture classification models that will outperform previously presented methods. In this work, transfer learning was applied to attain this goal. MobileNetV3 and InceptionV3 are the two pre-trained models employed. Brodatz, Kylberg, and Outex texture datasets were used to evaluate the models. The models achieved excellent results and achieved the objective in most cases. Classification accuracy obtained for the Kylberg dataset were 100% and 99.89%. For the Brodatz dataset, the classification accuracy obtained was 99.83% and 99.94%. For the Outex datasets, the classification accuracy obtained was 99.48% and 99.48%. The model outputs the corresponding label of the texture of the image.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Texture classification for visual data using transfer learning

Goyal

Sharma

2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…However, there are still challenges related to communication overhead, device reliability, and security in the FL framework, which require further investigation. In (15) , the authors address the challenge of traffic sign recognition in Pakistan using deep learning techniques. They began by highlighting the limitations of conventional image processing methods and emphasized the importance of CNNs in computer vision, with a focus on the availability of datasets.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

Uma,

Prateeksha,

Padmapriya

2024

IJST

View full text Add to dashboard Cite

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

show abstract

An Effective Traffic Sign Image Classification and Detection Using CNN

Mishra

Goyal

2022

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Pakistani traffic-sign recognition using transfer learning

Cited by 9 publications

References 38 publications

Texture classification for visual data using transfer learning

Texture classification for visual data using transfer learning

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

An Effective Traffic Sign Image Classification and Detection Using CNN

Contact Info

Product

Resources

About