An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN

Güney, Emin; Bayılmış, Cüneyt

doi:10.35377/saucis...1073355

Cited by 16 publications

(14 citation statements)

References 25 publications

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“…It helps to tune the moving of the cars. Support Vector Machines (SVMs) have been widely used for object detection and image classification [38]. SVMs have shown promising results in various applications, but they are limited by their inability to handle large datasets and the need for feature extraction.…”

Section: B Resultsmentioning

confidence: 99%

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

Abdrakhmanov,

Elemesova,

Zhussipbek

et al. 2023

IJACSA

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The accurate and real-time detection of road lanes is crucial for the safe navigation of autonomous vehicles (AVs). This paper presents a novel approach to lane detection by leveraging the capabilities of the Mask Region-based Convolutional Neural Network (Mask R-CNN) model. Our method adapts Mask R-CNN to specifically address the challenges posed by diverse traffic scenarios and varying environmental conditions. We introduce a robust, efficient, and scalable architecture for lane detection, which segments the lane markings and generates precise boundaries for AVs to follow. We augment the model with a custom dataset, consisting of images collected from different geographical locations, weather conditions, and road types. This comprehensive dataset ensures the model's generalizability and adaptability to real-world conditions. We also introduce a multi-scale feature extraction technique, which improves the model's ability to detect lanes in both near and far fields of view. Our proposed method significantly outperforms existing state-of-the-art techniques in terms of accuracy, processing speed, and adaptability. Extensive experiments were conducted on public datasets and our custom dataset to validate the performance of the proposed method. Results demonstrate that our Mask R-CNN-based approach achieves high precision and recall rates, ensuring reliable lane detection even in complex traffic scenarios. Additionally, our model's real-time processing capabilities make it an ideal solution for implementation in AVs, enabling safer and more efficient navigation on roads.

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Section: B Resultsmentioning

confidence: 99%

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

Abdrakhmanov,

Elemesova,

Zhussipbek

et al. 2023

IJACSA

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“…Herramientas Aplicadas 1) Makesense.ai: Es una herramienta en línea gratuita orientada al etiquetado de imágenes para su respectiva detección y reconocimiento. Esta herramienta facilita la selección de los nombres de las clases y estudiantes mediante un proceso óptimo con base en el conjunto [10]. Puesto que el rendimiento del algoritmo está claramente relacionado con la calidad de las imágenes entrenadas, ello facilita la preparación de una base de datos con imágenes etiquetadas.…”

Section: Metodologíaunclassified

“…2) YOLO v5: Su sigla proviene del inglés You Only Look Once (YOLO) que es un framework de detección de objetos en tiempo real de código abierto. Este nos proporciona una red neuronal convolucional; basada en Darknet, ya estructurada para la detección de objetos en imágenes o videos [10,11]. Existen diferentes versiones de este framework, en abril del 2020 se presentó una nueva versión Yolo v5 creada por Glenn Jocher.…”

Section: Metodologíaunclassified

University Student Control Detection System Based on Machine Learning and Artificial Intelligence

Calvo-Lavado¹,

Lopez-Carreño²,

Zarate-Perez³

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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The objective of this study was to program a school assistant based on artificial intelligence and integrated with surveillance cameras and loudspeakers in a university classroom. This was done to complement the supervision carried out by university teachers to identify student distractions, guarantee the proper use of masks, and evaluate student behaviors in class. The virtual assistant was developed using Python to generate audio warnings through a graphical interface built in the PyCharm environment. The results demonstrated the desired functionality of the virtual assistant, its ability to meet the requirements of a university classroom, and thereby the effectiveness of the YOLO v5 network andPyCharm used for the training, execution, construction, and implementation of the system.

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“…KIR is used to transform unstructured text outputs from ASR into structured texts that are understandable by machines. In automated pilot agent systems, the importance of KIR technology is akin to the role of object detection technology in autonomous driving systems 9 . As of now, some experts and scholars in the field of ATC have conducted research on related technologies.…”

Section: Introductionmentioning

confidence: 99%

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

Jiang,

Zeng,

Pan

et al. 2024

Sci Rep

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In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR.

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An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN

Cited by 16 publications

References 25 publications

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

University Student Control Detection System Based on Machine Learning and Artificial Intelligence

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

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