Motorcycle and Vehicle Detection for Applications in Road Safety and Traffic Monitoring Systems

Cruz, Gerson Gerard L.; Litonjua, Aaron; Juan, Alysia Noreen P. San; Libatique, Nathaniel Joseph C.; Tan, Marion Ivan L.; Honrado, Jaime Luis E.

doi:10.1109/ghtc55712.2022.9910992

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Road networks are a fundamental component of urban and rural infrastructure, playing a crucial role in promoting economic development and improving the quality of life for residents. Accurate extraction of road information holds significant practical application value in various fields, including urban planning [1][2][3], traffic monitoring [4][5][6], disaster emergency response [7][8][9], and environmental monitoring [10][11][12]. With the continuous advancement of remote sensing technology, we now have access to a greater amount of clear image data [13].…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

Liu,

Wu,

et al. 2024

Remote Sensing

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Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and environmental monitoring. With rapid development in the field of computational intelligence, particularly breakthroughs in deep learning technology, road extraction technology has made significant progress and innovation. This paper provides a systematic review of deep learning-based methods for road extraction from remote sensing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. According to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learning approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations. Additionally, this review summarizes the metrics used to evaluate the performance of road extraction models and the high-resolution remote sensing image datasets applied for road extraction. Finally, we discuss the main challenges and prospects for leveraging computational intelligence techniques to enhance the precision, automation, and intelligence of road network extraction.

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Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

Liu,

Wu,

et al. 2024

Remote Sensing

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Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

Sanjay S Tippannavar,

Yashwanth S D

2023

JTCSST

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Due to the increasing number of cars on the road and the exponential growth of traffic throughout the globe, regulating traffic has become crucial in the most industrialized countries. The development of technology has led to the current state of traffic management systems that comes with the ability to count, monitor, and predict the speed of vehicles in order to improve the transportation planning. This has also reduced the number of accidents that occur due to worsen traffic conditions. Road traffic surveys have been carried out manually for a long time since automated measures were not often employed due to the difficulty of installation. Machine learning in image processing is widely recognized as a significant approach for real-world applications such as traffic monitoring. The primary benefit of automated vehicle counting is that it allows for the management and evaluation of traffic in the urban transportation system. There are many methods employing distributed acoustic systems on intelligent transportation systems, including YOLO v4 and the Normalized Cross-correlation algorithm, which uses ultrasonic sensors and the algorithms ALPR, YOLO, GDPR, and CNN. The simplest method for identifying a vehicle is to gather information from sensors such as cameras, vibration detectors, ultrasound detectors, or acoustic detectors. These sensors are combined with the proper microcontrollers to determine the amount of traffic using the most recent data and theory. This review article is a quick reference for researchers working on safety-related traffic management systems.

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Motorcycle and Vehicle Detection for Applications in Road Safety and Traffic Monitoring Systems

Cited by 2 publications

References 6 publications

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

Real-Time Vehicle Identification for Improving the Traffic Management system-A Review

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