Automatic Vehicle Accident Detection and Classification from Images: A Comparison of YOLOv9 and YOLO-NAS Algorithms

Nusari, Ahmed Naji Musleh; Ozbek, Ibrahim Yucel; Oral, Emin Argun

doi:10.1109/siu61531.2024.10600761

2024 32nd Signal Processing and Communications Applications Conference (SIU) 2024

DOI: 10.1109/siu61531.2024.10600761

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Automatic Vehicle Accident Detection and Classification from Images: A Comparison of YOLOv9 and YOLO-NAS Algorithms

Ahmed Naji Musleh Nusari,

Ibrahim Yucel Ozbek,

Emin Argun Oral

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2024

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Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Karim,

Raza,

Alharthi

et al. 2024

WEVJ

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Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.

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Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Karim,

Raza,

Alharthi

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

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Automatic Vehicle Accident Detection and Classification from Images: A Comparison of YOLOv9 and YOLO-NAS Algorithms

Cited by 1 publication

References 13 publications

Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

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