Earticle

초록

영어

Object detection (OD) is a fundamental task in computer vision. However, progress is often hindered by limitations in existing datasets, including human annotation errors, reliance on manual annotation, missing annotations due to occlusion, and domain specificity. To address these challenges, this work proposes an automatically generated synthetic single-view dataset for OD. The dataset was generated in Unity by constructing a 3D virtual city with a single-camera surveillance system, providing diverse perspectives and calibrated viewpoints. Object metadata, including position and dimensions, was automatically extracted and projected into the 2D image plane to generate accurate bounding boxes. Annotations were normalized into YOLO format, with invalid boxes removed, resulting in a single-view dataset that is consistent, precise, and free from manual labeling errors, while still reflecting real-world challenges such as occlusion and object variation. Two versions of the dataset, original and refined, were created to evaluate the effect of bounding box quality on detection performance. An experimental evaluation using the YOLOv11 model demonstrated that the proposed dataset substantially improved detection performance, yielding notable gains in precision, recall, and mean average precision (mAP). These results underscore the importance of accurate dataset curation and highlight the potential of synthetic datasets to advance single-view OD in applications such as surveillance, autonomous systems, and robotics.

목차

Abstract
I. INTRODUCTION
II. LITERATURE
III. METHODOLOGY
A. Dataset Construction
B. Preprocessing
C. Object Detection Module
IV. EXPERIMENTAL RESULTS
A. Experimental Setup
B. Performance Analysis
V. CONCLUSION & FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Waqas Ahmad [ Sejong Univeristy Seoul, South Korea ]
• Mohib Ullah [ Sejong Univeristy Seoul, South Korea ]
• Muhammad Munsif [ Sejong Univeristy Seoul, South Korea ]
• Adnan Hussain [ Sejong Univeristy Seoul, South Korea ]
• Kaleem Ullah [ Sejong Univeristy Seoul, South Korea ]
• Amjid Ali [ Sejong Univeristy Seoul, South Korea ]
• Sung Wook Baik [ Sejong Univeristy Seoul, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004