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

Boujemaa, Kaoutar Sefrioui; Akallouch, Mohammed; Berrada, Ismaïl; Fardousse, Khalid; Bouhoute, Afaf

doi:10.1109/access.2021.3092821

Cited by 5 publications

(5 citation statements)

References 40 publications

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“…The comparison in the graphs clearly exhibiting the better performance of proposed algorithm by using YOLOv5. We also perform testing using different datasets [17,[60][61][62] for detection. We also collect images using mobile phones, camera and internet, which we have transformed into an unruffled dataset.…”

Section: Resultsmentioning

confidence: 99%

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A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Khalid,

Shah,

Sharif

et al. 2024

IEEE Access

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Traffic signs have great importance regarding smooth traffic flow and safe driving. However, due to many distractions and capricious factors, spotting and perceiving them may become hazardous. Traffic sign detection and recognition have gained popularity to put an end or to lessen the issue, and massive efforts have been realized in this regard. Despite considerable endeavors put together for traffic sign detection and recognition, there is a lack of attention in this area where these traffic signs contain text in them. A handful of studies may be found in state-of-the-art (SOTA) methods for text-based traffic sign detection, and particularly lesser for text recognition of detected text. The proposed method focuses on developing a robust semi-pipeline intelligent system to detect and understand text from traffic road signs boards in various weather conditions. For this purpose, a customized YOLOv5s is deployed for initial panel detection. Subsequently, MSER with preprocessing techniques is used for localization of text. Finally, OCR with NLP is utilized to recognize the text. The proposed method employed the ASAYAR dataset for training and different datasets for testing. The proposed approach produced satisfactory outcomes on them in contrast with SOTA approaches.

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Section: Resultsmentioning

confidence: 99%

“…This attribute of text-based road signs makes MSER an appropriate choice for text detection. MSER also not computationally costly rather, it is efficient, exhibiting complexity that is nearly linear [61]. For real time applications, linear complexity works as an exceptionally significant attribute.…”

Section: Text Localization Using Msermentioning

confidence: 99%

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Khalid,

Shah,

Sharif

et al. 2024

IEEE Access

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show abstract

“…Since the Chinese text lines are usually vertical and horizontal on text-based traffic panels, the presented technique created the text lines by the structural information and position of the characters. In [40], the author presented a novel public multi-task dataset for detecting text-based traffic panels. In [41], it is presented an end-to-end trainable deep neural network that can recognize multi-oriented text instances in adverse meteorological conditions.…”

Section: The Methods That Extract the Features Using Deep Learning Al...mentioning

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.

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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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“…To our knowledge, there are two datasets for Arabic traffic signals: ArTS [16] and ATTICA [17]. A developed labeled dataset of images of Arabic traffic signs, known as ArTS, was created at Prince Mohammad Bin Fahd University in Al-Khobar, Kingdom of Saudi Arabia.…”

Section: Datasetmentioning

confidence: 99%

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Khalifa,

Alayed,

Elbadawy

et al. 2024

Applied Sciences

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Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

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

Cited by 5 publications

References 40 publications

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

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