Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement
Abstract
Traffic density measurement is a critical component in traffic management and urban planning. This study addresses the challenge of accurately measuring traffic density by comparing the performance of the YOLOv8 segmentation method with the traditional HSV method. At the beginning of the abstract, we clearly present the problem of accurately measuring traffic density. The primary objective is to highlight the strengths and limitations of each method in terms of accuracy and reliability in traffic density estimation. The choice of segmenting the asphalt area rather than vehicle objects is justified by the need to understand how different segmentation approaches affect traffic density measurements. The HSV method involves converting images to the HSV color space, creating masks for specific areas, and measuring traffic density based on the asphalt area. This method, while straightforward, may not accurately capture the dynamic nature of vehicle movement. In contrast, the YOLOv8 segmentation method utilizes a deep learning approach to detect and segment vehicles, providing potentially more precise measurements. Experimental results from three locations demonstrate varying levels of traffic density. The YOLOv8 method results in a graph with a wavy pattern, reflecting the more detailed detection of vehicles. Conversely, the HSV method produces a linear pattern, indicating a more consistent but potentially less detailed measurement. Quantitative analysis shows that Location 2 has a higher traffic density compared to Locations 1 and 3, as indicated by the average number of detected vehicles per frame. This study provides a comprehensive understanding of the differences between HSV and YOLOv8 segmentation methods for traffic density measurement. The findings suggest that while YOLOv8 offers more detailed and dynamic detection, the HSV method provides a simpler yet effective approach for certain applications.
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