AUT-UTP: Urban traffic panel detection and recognition dataset

Korghond, Navid Khazaee; Safabakhsh, Reza

doi:10.1109/iraniancee.2016.7585791

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yellow, green, light green, white, dark red, and dark blue are examples of the linguistic values assigned to each pixel. In [45], presented a dataset including videos of traffic panels recorded by a moving vehicle in municipal regions. They categorized the video sequences based on their features in a way that researchers could select the piece of data that helps their research purpose.…”

Section: The Methods That Extract the Features In A Traditional Waymentioning

confidence: 99%

See 1 more Smart Citation

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: The Methods That Extract the Features In A Traditional Waymentioning

confidence: 99%

“…As far as we know, few benchmark datasets for textbased traffic panel detection are publicly available [45,40]. Hence, this study gathered a novel public dataset all over the city of Tehran-Iran and called it 'Persian Text-Based Traffic Panel Dataset' [46].…”

Section: Text-based Traffic Panelsmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

View full text Add to dashboard Cite

show abstract

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

et al. 2021

View full text Add to dashboard Cite

Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS). They can significantly contribute in enhancing road safety by optimizing the driving experience. However, these tasks might face two major challenges. First, the lack of suitable and good quality datasets. Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text. Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued. In this paper, we address this issue by introducing ATTICA a , a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition. Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes. In order to examine the utility and advantages of our dataset, we adopt stateof-the-art deep learning based approaches. The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.

show abstract