Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement
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, w...
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/114169 |
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| author | Prof. I Gede Pasek Suta Wijaya Muhamad Nizam Azmi Ario Yudo Husodo |
| author_facet | Prof. I Gede Pasek Suta Wijaya Muhamad Nizam Azmi Ario Yudo Husodo |
| author_sort | Prof. I Gede Pasek Suta Wijaya |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-52b0fda4f990459d9fb1704b121527d4 |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-52b0fda4f990459d9fb1704b121527d42025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-01150213414810.24843/LKJITI.2024.v15.i02.p06114169Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density MeasurementProf. I Gede Pasek Suta Wijaya0Muhamad Nizam AzmiArio Yudo Husodo[Scopus ID: 23494142600, h-index: 3], Jurusan Sistem Komputer dan Informatika, Universitas MataramTraffic 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.https://ojs.unud.ac.id/index.php/lontar/article/view/114169 |
| spellingShingle | Prof. I Gede Pasek Suta Wijaya Muhamad Nizam Azmi Ario Yudo Husodo Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement Lontar Komputer |
| title | Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement |
| title_full | Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement |
| title_fullStr | Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement |
| title_full_unstemmed | Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement |
| title_short | Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement |
| title_sort | comparative analysis of yolov8 and hsv methods for traffic density measurement |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/114169 |
| work_keys_str_mv | AT profigedepaseksutawijaya comparativeanalysisofyolov8andhsvmethodsfortrafficdensitymeasurement AT muhamadnizamazmi comparativeanalysisofyolov8andhsvmethodsfortrafficdensitymeasurement AT arioyudohusodo comparativeanalysisofyolov8andhsvmethodsfortrafficdensitymeasurement |