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A Study on Traffic Vulnerable Detection Using Object Detection-Based Ensemble and YOLOv5

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(1), pp.61-68
  • DOI : 10.9708/jksci.2024.29.01.061
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 23, 2023
  • Accepted : December 22, 2023
  • Published : January 31, 2024

Hyun-Do Lee 1 Sun-Gu Kim 1 Seung-Chae Na 1 함지율 1 Chanhee Kwak 1

1강남대학교

Accredited

ABSTRACT

Despite the continuous efforts to mitigate pedestrian accidents at crosswalks, the problem persist. Vulnerable groups, including the elderly and disabled individuals are at a risk of being involved in traffic incidents. This paper proposes the implementation of object detection algorithm using the YOLO v5 model specifically for pedestrians using assistive devices like wheelchairs and crutches. For this research, data was collected and utilized through image crawling, Roboflow, and Mobility Aids datasets, which comprise of wheelchair users, crutch users, and pedestrians. Data augmentation techniques were applied to improve the model's generalization performance. Additionally, ensemble techniques were utilized to mitigate type 2 errors, resulting in 96% recall rate. This demonstrates that employing ensemble methods with a single YOLO model to target transportation-disadvantaged individuals can yield accurate detection performance without overlooking crucial objects.

Citation status

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