Earticle

초록

영어

This study focuses on the design and performance evaluation of a lightweight object detection model, YOLOv11BiFormer, for the efficient detection of road surface defects such as alligator cracks, longitudinal cracks, potholes, and transversal cracks. To enhance computational efficiency and improve the detection of small-scale objects, the proposed model integrates the BiFormer block and C2f module into the existing YOLOv11 architecture. The dataset used for training and evaluation consists of 7,238 highresolution images, which were evenly divided into 5,065 training images and 2,137 validation images across the four defect categories. Experimental results show that the YOLOv11BiFormer model outperforms the original YOLOv11 in multiple metrics: mAP 0.5 improved from 0.522 to 0.546, mAP 0.5:0.95 increased from 0.691 to 0.703, and precision rose from 0.462 to 0.497. Furthermore, the number of parameters and model size were reduced from 2,582,932 to 2,464,956 and from 5.5MB to 5.2MB, respectively. Visual analysis also demonstrated superior detection accuracy and clearer boundary identification with the BiFormer-enhanced model.These findings suggest that the proposed YOLOv11 BiFormer model is well-suited for real-time road defect detection in mobile devices and edge computing environments, offering a promising solution for intelligent transportation systems and automated infrastructure inspection.

목차

Abstract
1. Introduction
2. Research Objectives and Model Design
3. Experiments and Results
4. Conclusion
References

키워드

저자

• Haoran Hu [ Doctoral program, Department of Computer Engineering, Honam University, Korea ]
• Lee Hye-Min [ Researcher, JTOMORROWONE CO.,LTD. ]
• Sang-Hyun Lee [ Associate Prof., Department of Computer Engineering, Honam University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380