Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Author:

Karim Abdul1,Raza Muhammad Amir1ORCID,Alharthi Yahya Z.2ORCID,Abbas Ghulam3ORCID,Othmen Salwa4,Hossain Md. Shouquat5,Nahar Afroza6,Mercorelli Paolo7ORCID

Affiliation:

1. Department of Electrical Engineering, Mehran University of Engineering and Technology, SZAB Campus Khairpur Mir’s, Khairpur 66020, Sindh, Pakistan

2. Department of Electrical Engineering, College of Engineering, University of Hafr Albatin, Hafr Al Batin 39524, Saudi Arabia

3. School of Electrical Engineering, Southeast University, Nanjing 210096, China

4. Department of Computers and Information Technologies, College of Sciences and Arts Turaif, Northern Border University, Arar 91431, Saudi Arabia

5. Department of Electrical and Electronic Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh

6. Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh

7. Institute for Production Technology and Systems (IPTS), Leuphana Universität Lüneburg, 21335 Lüneburg, Germany

Abstract

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.

Publisher

MDPI AG

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