인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보기술학회 Proceedings of The International Workshop on Future Technology The Proceedings of ADINTECH 2025
- 발행연도
- 2025.8
- 수록면
- 33 - 38 (6page)
이용수
초록· 키워드
The increasing accumulation of plastic waste over recent decades has become a pressing environmental concern. Although public awareness of recycling has grown, studies show that up to 75% of recyclable municipal solid waste is still discarded in landfills. To address this issue, we propose a deep learningbased pipeline for plastic waste sorting that combines the YOLOv8 object detection framework with contrast-enhanced image preprocessing using CLAHE (Contrast-Limited Adaptive Histogram Equalization). CLAHE is applied independently to each RGB channel of the input image and blended with the original to enhance visual features while preserving color fidelity. The proposed system is designed to detect and classify multiple plastic types (PE, PET, PP, PS) from real-world images. Experimental results demonstrate that the model achieves a mean Average Precision (mAP@0.5) of 0.878 and an F1-score of 0.83 at the optimal confidence threshold, indicating robust detection performance. Notably, the balanced blending strategy outperformed both unprocessed and fully enhanced inputs. These results suggest that the integration of adaptive contrast enhancement can significantly improve detection robustness in practical waste sorting systems. Our approach offers a scalable and accurate solution for real-time deployment in automated recycling facilities, contributing to improved plastic recovery and sustainable waste management.
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목차
- Abstract
- 1 Introduction
- 2 Method
- 3 Experiment
- 4 Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-25-02-093929071
