Abstract. This article pioneers the fusion of advanced computer vision, and environmental science in order to be a starting point in ecological tasks and environmental benefits. Utilizing state-of-the-art tools like YOLOv7 and innovative algorithms, the study achieves unmatched accuracy in vehicle identification, classification, tracking, and speed analysis. By optimizing YOLOv7-e6e-1280 architecture using TensorRT and reduced precision, real-time analysis becomes possible without compromising accuracy. The integration of the Vanishing Point Principle for road zoning and zone-based speed calculation provides nuanced insights into driving behaviors. Detailed vehicle classification and robust tracking offer valuable data for urban planning and ecological studies. This approach increase our potential in vehicular analysis, setting new standards for research in urban development, transportation, and environmental science.
Optimized zone-based vehicle speed estimation and classification
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Speed estimation and length based vehicle classification from freeway single-loop detectors
Online Contents | 2009
|Speed estimation and length based vehicle classification from freeway single-loop detectors
Online Contents | 2009
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