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Research Paper

A Study on the Separation Efficiency Analysis of Waste Battery Scraps Based on Deep Learning Image Recognition

딥러닝 기반 영상인식을 활용한 배터리 파쇄물의 선별률 분석 연구

Kyeonghyeon Lee12
,
Hansol Lee2
,
Hoon Lee12*
이 경현12
,
이 한솔2
,
이 훈12*
1Resources Engineering, University of Science and Technology, Daejeon 34113, Korea
2Resources Utilization Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
1과학기술연합대학원대학교 자원공학과
2한국지질자원연구원 자원활용연구본부
*Corresponding Author
31 August 2025. pp. 55-63
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Abstract

In this study, an image recognition-based analysis method was applied to by-products generated from the battery crushing process, aiming to improve the recycling efficiency of complex waste streams. A deep learning model for material classification was developed based on YOLOv8, and particle areas were quantified using Canny edge-based contour detection along with baseline calibration techniques. Approximately 7,000 images were utilized for training, and data augmentation and correction algorithms enabled the model to achieve a high object detection accuracy with an mAP of 0.971. Based on the quantified particle area and the calibrated mass per unit area (g/mm2), the mass of each particle was estimated. These values were used to calculate recovery and grade, and the predicted results were compared with experimentally measured data. Recovery exceeded 95% across all material types, and the correspondence rate for grade reached approximately 90%, confirming the reliability of the image-based analysis. The study demonstrates that material classification and particle property analysis based on image processing enable accurate evaluation of separation recovery and product grade. In addition, the method shows potential for rapid and efficient assessment compared to conventional equipment and can be further expanded into real-time quality monitoring and process optimization systems.

Keywords
waste batteries
object detection
edge detection
recovery and grade analysis
mass estimation

본 연구에서는 배터리 파쇄 공정 산물에 대해 영상 인식 기반 분석 기법을 적용하여, 복합 폐기물의 재활용 효율 향상을 도모하고자 하였다. YOLOv8을 기반으로 재질 인식을 위한 학습 모델을 구축하였으며, Canny edge 기반 윤곽선 검출 및 기준선 보정 기법을 통해 입자의 면적을 정량화하였다. 약 7,000개의 이미지를 학습 데이터로 활용하였으며, 데이터 증강 및 보정 알고리즘을 이용하여 mAP 0.971의 높은 객체 인식 정확도를 달성하였다. 입자 면적 정보를 기반으로 단위 면적당 질량(g/mm2)을 통해 입자별 질량을 추정하였으며, 이를 통해 영상 기반 회수율 및 순도를 계산하였다. 예측값은 실험을 통해 얻은 실측값과 비교되었으며, 회수율은 모든 재질에서 95% 이상, 순도는 약 90% 수준의 정합률을 보여 영상 기반 분석의 신뢰성을 입증하였다. 영상인식 기반의 재질 인식 및 입자 특성 분석을 통한 선별 회수율 및 순도 계산에 대한 신뢰도를 확보하였다. 연구는 영상 기반 분석이 기존 장비 대비 신속하고 효율적임을 보여주며, 향후 실시간 품질 모니터링 및 공정 최적화 시스템으로의 확장 가능성을 제시한다.

키워드
폐배터리
객체 인식
윤곽선 검출
회수율 및 순도 분석
질량 추정
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Information
  • Publisher :The Korean Institute of Resources Recycling
  • Publisher(Ko) :한국자원리싸이클링학회
  • Journal Title :Resources Recycling
  • Journal Title(Ko) :자원리싸이클링
  • Volume : 34
  • No :4
  • Pages :55-63
  • Received Date : 2025-07-25
  • Revised Date : 2025-08-18
  • Accepted Date : 2025-08-21
  • DOI :https://doi.org/10.7844/kirr.2025.34.4.55
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