A Transfer Learning based approach for Pakistani Traffic-sign Recognition; using ConvNets

Nadeem, Zain; Samad, Abdul; Abbas, Zulkafil; Massod, Janzaib

doi:10.1109/icecube.2018.8610979

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…They presented a new traffic panel recognition method via incorporating a lightweight superclass detector with a refinement classifier [59]. It is presented a new dataset in Pakistan and used transfer learning to recognize the traffic panels [21].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

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Lately, traffic panel detection has been engrossed by academia and industry. This study proposes a new categorization method for traffic panels. The traffic panels are classified into three classes: symbol-based, text-based, and supplementary/additional traffic panels. Although few types of research have investigated text-based traffic panels, this type is considered in detail in this study. However, there are many challenges in this type of traffic panel, such as having different languages in different countries, their similarity with other text panels, and the lack of suitable quality datasets. The panels need to be detected first to obtain a reasonable accuracy in recognizing the text. Since there are few public text-based traffic panels datasets, this study gathered a novel dataset for the Persian textbased traffic panels all over the streets of Tehran-Iran. This dataset includes two collections of images. The first collection has 9294 images, and the latter has 3305 images. The latter dataset is more monotonous than the first one. Thus, the latter is utilized as the main dataset, and the first is used as an additional dataset. To this end, the algorithm uses the additional dataset for pre-training and the main datasets for training the network. The tiny YOLOv3 algorithm that is fast and has low complexity compared to the YOLOv3 is used for pre-training, training, and testing the data to examine the utility and advantages of the data. The K-fold cross-validation procedure is used to estimate the model's skill on the new data. It achieves 0.973 for Precision, 0.945 for Recall, and 0.955 for Fmeasure.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

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“…In previous studies, all this work was done on the traffic rules and signs for many countries, but in Pakistan, [30] same work was done on signs recognition using Convolutional Neural Networks and transfer learning. In this study, the authors implemented a model using CNN to recognize traffic signs.…”

Section: Related Workmentioning

confidence: 99%

“…This section also describes how to change the information selection and template design procedures to create a template that best meets our needs. Over time, data has been collected and downloaded from different sources like google and Islamabad traffic police website and some cities [11] as this data also includes the previous collection which was done by Nadeem et al [30] in their work, which includes cities like Quetta, Islamabad and Karachi, Pakistan, but not limited to only these cities as motorway and Pakistan national highway dataset can also be collected. Finally, although the number of images collected as part of the Pakistani material is small, the images described in table 1.…”

Section: Pakistan Traffic Signs Datasetmentioning

confidence: 99%

“…As shown in Figure 8, It is a Pakistani dataset just like the German dataset. [ The 359 images were collected by Nadeem et al [30] and 579 total images were gathered around by collected 220 new images from different sources and then physically created to filter out background information from unwanted images. All images are organized according to correspondence that includes the names of the images.…”

Section: Pakistan Traffic Signs Datasetmentioning

confidence: 99%

“…These experiments found several positive results that encouraged researchers to do more research in this area to increase accuracy. After all, autonomous vehicles are the present for the future of the world of robotics and automation [30]. Currently, autonomous vehicles are one of the biggest industries in which many well-known industrial products are available in the market.…”

Section: Introductionmentioning

confidence: 99%

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Image Classification using CNN for Traffic Signs in Pakistan

Azeem¹,

Ali²

2021

Preprint

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The autonomous automotive industry is one of the largest and most conventional projects worldwide, with many technology companies effectively designing and orienting their products towards automobile safety and accuracy. These products are performing very well over the roads in developed countries. But can fail in the first minute in an underdeveloped country because there is much difference between a developed country environment and an underdeveloped country environment. The following study proposed to train these Artificial intelligence models in environment space in an underdeveloped country like Pakistan. The proposed approach on image classification uses convolutional neural networks for image classification for the model. For model pre-training German traffic signs data set was selected then fine tuned on Pakistan's dataset. The experimental setup showed the best results and accuracy from the previously conducted experiments. In this work to increase the accuracy, more dataset was collected to increase the size of images in every class in the data set. In the future, a low number of classes are required to be further increased where more images for traffic signs are required to be collected to get more accuracy on the training of the model over traffic signs of Pakistan's most used and popular roads motorway and national highway, whose traffic signs color, size, and shapes are different from common traffic signs.

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