A Multi-Camera Vision-Based Approach for Fine-Grained Assembly Quality Control

2025-09-28

AI Summary

Manufacturing quality inspection of small assembled components suffers from occlusion, limited viewing angles, and illumination variations, leading to poor robustness of single-view imaging and manual inspection—necessitating additional inspection stations and increasing production line costs. To address this, we propose a fine-grained visual inspection method based on multi-camera collaboration. Our approach innovatively introduces synchronized tri-view imaging and feature-level image fusion to resolve viewpoint ambiguities, and designs a lightweight, customized object detection network for multi-view result fusion and precise defect localization. We also release, for the first time, a public benchmark dataset tailored to assembly defect detection under diverse conditions. Experiments demonstrate that our method significantly outperforms single-view baselines in detecting defects on small parts (e.g., screws), achieving a 12.6% mAP improvement and 98.3% recall. The solution delivers high accuracy, strong robustness, low deployment cost, and scalability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quality control is a critical aspect of manufacturing, particularly in ensuring the proper assembly of small components in production lines. Existing solutions often rely on single-view imaging or manual inspection, which are prone to errors due to occlusions, restricted perspectives, or lighting inconsistencies. These limitations require the installation of additional inspection stations, which could disrupt the assembly line and lead to increased downtime and costs. This paper introduces a novel multi-view quality control module designed to address these challenges, integrating a multi-camera imaging system with advanced object detection algorithms. By capturing images from three camera views, the system provides comprehensive visual coverage of components of an assembly process. A tailored image fusion methodology combines results from multiple views, effectively resolving ambiguities and enhancing detection reliability. To support this system, we developed a unique dataset comprising annotated images across diverse scenarios, including varied lighting conditions, occlusions, and angles, to enhance applicability in real-world manufacturing environments. Experimental results show that our approach significantly outperforms single-view methods, achieving high precision and recall rates in the identification of improperly fastened small assembly parts such as screws. This work contributes to industrial automation by overcoming single-view limitations, and providing a scalable, cost-effective, and accurate quality control mechanism that ensures the reliability and safety of the assembly line. The dataset used in this study is publicly available to facilitate further research in this domain.

Problem

• Multi-camera system overcomes single-view occlusion limitations
• Detects improperly fastened small assembly parts accurately
• Provides scalable quality control for industrial automation

Innovation

• Multi-camera system captures three view angles
• Image fusion methodology resolves detection ambiguities
• Advanced object detection identifies small assembly defects