TY - JOUR

T1 - Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

AU - Karim, Abdul

AU - Raza, Muhammad Amir

AU - Alharthi, Yahya Z.

AU - Abbas, Ghulam

AU - Othmen, Salwa

AU - Hossain, Md Shouquat

AU - Nahar, Afroza

AU - Mercorelli, Paolo

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024/9

Y1 - 2024/9

N2 - 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.

AB - 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.

KW - object detection

KW - object tracking

KW - sustainable transportation

KW - traffic incident

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85205228215&partnerID=8YFLogxK

U2 - 10.3390/wevj15090382

DO - 10.3390/wevj15090382

M3 - Journal articles

AN - SCOPUS:85205228215

VL - 15

JO - World Electric Vehicle Journal

JF - World Electric Vehicle Journal

SN - 2032-6653

IS - 9

M1 - 382

ER -