Iraqi Traffic Signs Detection Based On Yolov5

Aggar, Ammar; Rahem, Abd Alrazak; Zaiter, Mohammed Joudah

doi:10.1109/aca52198.2021.9626821

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…After that, these images will be processed and classified by the computer vision model stored in Rpi, before being sent to the IoT cloud. In addition, RPi 4 model B+ 8 GB was proposed to detect traffic signs in realtime via using a deep learning model [8]. The images are gathered from the traffics via a RPi camera, which are then predicted by the model stored in RPi.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Implementing a Text-to-Speech synthesis model on a Raspberry Pi for Industrial Applications

Mandeel¹,

Aggar²,

Al-Radhi³

et al. 2023

1st Workshop on Intelligent Infocommunication Networks, Systems and Services

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Text-to-Speech (TTS) technology produces human-like speech from input text. It has recently acquired prominence by applying deep neural networks. Nowadays, endto-end TTS models produce highly natural synthesized speech but require extremely high computational resources. Deploying such high-quality TTS models in a real-time environment has been a challenging problem due to the limited resources of embedding systems and cell phones. This paper demonstrated the implementation of an end-to-end TTS model (FastSpeech 2) in an embedded device (Raspberry Pi4 B+). The objective experimental results showed that the TTS model is compatible with the Raspberry Pi (RPi) with high-quality synthesized speech and acceptable performance in terms of processing speed. Our proposed model could be used in many real-life applications if used together with a mechanism for caching, such as railway announcements and industrial purposes.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Implementing a Text-to-Speech synthesis model on a Raspberry Pi for Industrial Applications

Mandeel¹,

Aggar²,

Al-Radhi³

et al. 2023

1st Workshop on Intelligent Infocommunication Networks, Systems and Services

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“…The dataset for traffic sign detection should consider as many situations as possible so that the model can be more robust and work well in a variety of situations. Although some teams [2] used their local datasets, most of them [3][4][5] used datasets called GTSDB German data set, tt100k data set, and CCTSDB dataset for the training the proposed algorithms or models. In the following subsections, these datasets that are usually used in this case are introduced in detail to illustrate their characteristics and advantages.…”

Section: Datasetmentioning

confidence: 99%

Traffic Sign Detection Based on Deep Learning Methods

Huang¹,

Wang²,

Yang³

et al. 2022

Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)

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This article gives a brief overview of a few current research on traffic signs detection, which briefly reviews the concept and structure of traffic signs detection in the last decade. The methodology varies in different ways which is generally separated into two exact dimensions. The first one is the traditional method using the theory of computer vision with machine learning to detect the traffic signs, while the other one uses deep learning to train the model to detect the objects. In recent years, the methods based on deep learning have gradually replaced the traditional methods since they can extract features from traffic signs better and do predictions. Therefore, this paper mainly focuses on the deep learning methods for traffic signs detection and reviews previous work and their corresponding datasets and performance. The results based on different methods are compared. Finally, we made a conclusion based on this review.

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