Deep Neural Networks Based Object Detection for Road Safety Using YOLO-V3

Tattari, Jalaja; Donthi, Vineeth Reddy; Mukirala, Dheeraj; Kour, Sunpreet

doi:10.1007/978-981-16-0878-0_71

Smart Computing Techniques and Applications

2021

DOI: 10.1007/978-981-16-0878-0_71

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Deep Neural Networks Based Object Detection for Road Safety Using YOLO-V3

Jalaja Tattari¹,

Vineeth Reddy Donthi²,

Dheeraj Mukirala³

et al.

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2024

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Cited by 2 publications

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Performance Analysis of Different Types of Nn Models for Target Recognition

Tsiunyk,

Muliarevych

2024

ACPS

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The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, the research is aimed at developing a robust methodology for assessing the performance of these NN models. Through meticulous analysis, this study aims to provide insights into the strengths and weaknesses of each model, facilitating informed decision-making for practical applications1. This paper presents the process of designing and conducting the performance analysis. The study discusses the implications of the findings for future developments in target recognition systems.

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Performance Analysis of Different Types of Nn Models for Target Recognition

Tsiunyk,

Muliarevych

2024

ACPS

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Performance Evaluation and Optimization of Yolov8 Neural Network Models for Target Recognition

Tsiunyk,

Muliarevych

2024

CSN

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The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m, and YOLO models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, Python, and roboflow 3.0 FAST, the research aims to develop a robust methodology for assessing the performance of these NN models. The methodology includes the design and implementation of experiments to measure key metrics such as accuracy, speed, and resource utilization. Through meticulous analysis, this study aims to provide insights into the strengths and weaknesses of each model, facilitating informed decision-making for practical applications1. This paper presents the process of designing and conducting the performance analysis, highlighting the rationale behind the selection of specific technologies and methodologies. Furthermore, the study discusses the implications of the findings for future developments in target recognition systems. Keywords: yolov8, YOLO, OpenCV, NN model.

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Deep Neural Networks Based Object Detection for Road Safety Using YOLO-V3

Cited by 2 publications

References 7 publications

Performance Analysis of Different Types of Nn Models for Target Recognition

Performance Analysis of Different Types of Nn Models for Target Recognition

Performance Evaluation and Optimization of Yolov8 Neural Network Models for Target Recognition

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