Computer Vision for Defect Detection, Segmentation and Quality Control in Manufacturing Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 18
Special Issue Editors
Interests: digital assembly and manufacturing; thin-walled structure assembly; machine vision; vision measurement and detection
Special Issue Information
Dear Colleagues,
Recently, smart manufacturing systems increasingly began relying on automation to ensure high product quality, enhance efficiency, and reduce costs. Computer vision has emerged as a cornerstone technology for automated quality control, replacing subjective and fatiguing manual inspection. The ability to automatically detect, segment, and classify defects in real time is critical across various industries, including aerospace manufacturing, electronics, automotive, textile, and pharmaceutical ones. However, challenges remain due to the high diversity of defect types, subtle visual characteristics, complex surface textures, and the demand for robust, high-speed inspection systems.
This Special Issue aims to gather the latest cutting-edge research and developments in computer vision for defect detection, segmentation, and quality control in manufacturing. We invite contributions that address the aforementioned challenges by proposing novel algorithms, systems, and applications. The scope of this Special Issue aligns perfectly with the Electronics journal's focus on industrial electronics and applied control systems. We seek high-quality original research articles and comprehensive reviews.
Research areas may include (but are not limited to) the following:
- Deep learning-based methods for defect detection and segmentation.
- Unsupervised and semi-supervised learning for anomaly detection with limited labeled data.
- Few-shot and zero-shot learning for detecting novel defect types.
- Case studies and system integration of vision-based quality control in manufacturing.
We look forward to receiving your contributions.
Dr. Biao Mei
Dr. Haijin Wang
Guest Editors
Manuscript Submission Information
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Keywords
- computer vision
- defect detection
- defect segmentation
- quality control
- smart manufacturing
- industrial automation
- deep learning
- machine vision
- anomaly detection
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