Computer Vision and Sensing Technologies for Industrial Quality Inspection: 3rd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 5 December 2026 | Viewed by 547
Special Issue Editors
Interests: computer vision; optical inspection; quality management; automated industrial inspection
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; computer vision; signal filtering; artificial intelligence; grey system with applications
Special Issues, Collections and Topics in MDPI journals
Interests: ergonomics; work physiology; human-computer interface
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Although quality inspections play an essential role in a successful operation today, finding effective ways to carry out them can be a challenge. Combined with advanced computer vision and sensing technologies, quality inspection can become an essential tool for various intelligent applications in smart manufacturing and production, such as object detection, classification, tracking and counting. The trend is to reach human-level precision or more in quality inspection with automation. Computer vision-based applications minimize human intervention, optimize operational efficiency and reduce labor costs. In addition, new sensing technologies have provided us with an excellent ability to measure, inspect, sort and grade products effectively and efficiently.
This Special Issue calls for research papers through use cases of artificial intelligence techniques and showcases the need to optimize algorithms, inference frameworks and hardware accelerators to obtain good performance in quality inspection. It mainly focuses on computer vision and sensing technologies for industrial quality inspection, including, but not limited to, imaging techniques, image processing methods, vision systems and system optimization. Industrial inspection papers are also welcome, such as quality inspection with machine learning and data-driven methods. Both review articles and original research papers are sought in this Special Issue.
Prof. Dr. Hong-Dar Lin
Prof. Dr. Cheng-Hsiung Hsieh
Prof. Dr. Hsin-Chieh Wu
Guest Editors
Manuscript Submission Information
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Keywords
- computer vision
- sensing technologies
- industrial quality inspection
- automatic optical inspection
- artificial intelligence techniques
- machine learning
- deep learning
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