Crowdsensing monitoring
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Date
2024-06
Journal Title
Journal ISSN
Volume Title
Publisher
University of Mohamed Boudiaf, M’sila
Abstract
The collaborative of crowdsensing and advanced sensing technologies can vastly improve
environmental and infrastructure monitoring in cities through creative applications. Novel use
cases tracked important metrics like air quality, noise levels and traffic patterns to provide
communities with valuable data. In this work, propose a Kinect sensor that leverage computer
vision for detecte obstacles in the roads.with adopt YOLO-v8 as obstacle detector,added a
series of methods proposed for road obstacle detection, like a self-driving car system and
Kinect approach that effectively mapped hazards in 3D for navigation safety. Based on those
methodes we are going to integrated to generate system in the futures. Results indicated
addressing limitations of traditional solutions by enabling timely, comprehensive data collection,
bringing significant benefits in safety, costs, transportation resilience and urban management.
Overall,this work is helping advance the potential of collaborative digital tools to revolutionize
smarter, safer mobility for all through continuous learning.
Description
Keywords
Road obstacle detection, Self-Driving cars, Microsoft kinect sensor, Obstacle avoidance systems, Crowdsensing, YOLOv8, Advanced sensor fusion, 3D mapping, Real-time detection accuracy, Depth image enhancement