데이터셋과 코드 Dataset & Code

역할: 데이터셋 구성 및 활용 연구(creator)

특징

• A large-scale dataset for semantic segmentation, comprising 10,836 images with pixel-level annotations, designed to advance automated quality inspection in the textile and apparel industry

• Total images: 10,836 images (derived and filtered from the original StitchingNet)

저장소 링크: https://github.com/hyungjungkim/StitchingNet-Seg 

역할: 운영 및 활용 연구(maintainer)

주관/지원 기관: 호전실업(주)(creator)

특징

• 11 가지 섬유-실 조합에 대해, 정상과 10가지 주요 봉제 불량 유형을 포함하는 산업용 봉제 공정 이미지 약 13,000 장을 제공

저장소 링크: https://www.kaggle.com/datasets/hyungjung/stitchingnet-dataset 

역할: 데이터셋 수집 및 활용 연구(creator)

특징

• Total: 327 images, Normal case: 161 images, Defective case (Spaghetti-shape error): 166 images

저장소 링크: https://github.com/hyungjungkim/systematic-deep-transfer-learning-for-fdm-defect-detection 

역할: 운영 및 활용 연구(maintainer)

특징

• Simplify3D, Prusa3D Help, All3DP 온라인 커뮤니티의 기술 문서를 직접 정리하여 제

저장소 링크: https://github.com/hyungjungkim/3D-printing-troubleshooting-guide

역할: 코드 개발 및 활용 연구(creator)

특징

• This code contains a systematic deep transfer learning method based on a small dataset to detect defects of the spaghetti-shape error in the FDM process.

저장소 링크: https://github.com/hyungjungkim/systematic-deep-transfer-learning-for-fdm-defect-detection 

역할: 코드 개발 및 활용 연구(creator)

특징

• This repository contains an appropriate status monitoring tool, KEM (Keep an Eye on your Machine). This tool can be used easily and affordably for such as Small and Medium-sized Enterprises (SMEs) in the manufacturing sector or developing countries using a low-cost vision sensor, such as a webcam, and open-source technologies, including OpenCV, Tesseract , and Python language.

저장소 링크: (Under reconstruction)