Visual Quality Score Assessment of Large White Goods in Remanufacture with Multi-View Deformable-DETR
Remanufacturing large white goods is essential for a circular economy, yet visual quality assessment remains a manual bottleneck for training and pricing. Conventional detection methods require extensive annotation and struggle with small defects in high-resolution multi-view data. We present a multi-view framework based on Deformable-DETR for automated quality scoring that aggregates information across redundant views to extract fine-grained features. To enhance robustness with limited labels, we employ self-supervised pretraining followed by supervised fine-tuning on expert-annotated scores. Additionally, a linear projection over frozen feature maps identifies regions of interest to explain model decisions. Evaluated on an industrial multi-view dataset, our approach delivers precise quality assessments while reducing reliance on manual annotation and per-part customization, enabling scalable and transparent inspection for remanufacturing lines.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Seeing Like a Designer Without One: A Study on Unsupervised Slide Quality Assessment via Designer Cue Augmentation
We present an unsupervised slide-quality assessment pipeline that combines seven expert-inspired visual-design metrics (whitespace, colorfulness, edge density, brightness contrast, text density, color harmony, layout bal…
Statistical evaluation of visual quality metrics for image denoising
This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise remov…
DenoisingImage DenoisingVulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
No-Reference Video Quality Assessment (NR-VQA) plays an essential role in improving the viewing experience of end-users. Driven by deep learning, recent NR-VQA models based on Convolutional Neural Networks (CNNs) and Tra…
Video Quality AssessmentVisual Question Answering (VQA)AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment
Many video-to-audio (VTA) methods have been proposed for dubbing silent AI-generated videos. An efficient quality assessment method for AI-generated audio-visual content (AGAV) is crucial for ensuring audio-visual qualit…
DeQA-Doc: Adapting DeQA-Score to Document Image Quality Assessment
Document quality assessment is critical for a wide range of applications including document digitization, OCR, and archival. However, existing approaches often struggle to provide accurate and robust quality scores, limi…
Document Image Quality AssessmentImage Quality AssessmentOptical Character Recognition (OCR)