About this Research Topic
The research on artificial intelligence (AI) in visual inspection is advancing rapidly and has become a core topic in computer vision. Visual inspection typically involves tasks such as object detection, image classification, semantic segmentation, and action recognition. The integration of AI has significantly enhanced the performance of these tasks, which have a broad spectrum of applications in industrial production, aerospace, medicine, and other fields. Traditional visual inspection methods rely on manually designed feature extractors, which are often effective for specific application scenarios but lack universality. While traditional visual inspection methods play an important role in certain application scenarios, their limitations in the face of complex scenes, diverse data, and real-time requirements have prompted researchers to turn to artificial intelligence technology to overcome these challenges and promote continuous progress in the field of visual inspection.
This research topic aims to rigorously investigate the future development trajectories of Artificial Intelligence (AI) in the domain of visual inspection, encompassing a comprehensive spectrum of applications. Central areas of focus include quality control and inspection, defect detection and tracking, non-destructive testing, and robotic vision for smart manufacturing, healthcare, agriculture, and autonomous vehicles. The primary objective is to enhance the operational efficiency and diagnostic accuracy of visual inspection systems through the integration of advanced AI methodologies. Furthermore, this research seeks to extend the boundaries of human capabilities through cutting-edge technologies by enabling more precise and reliable detection, classification, and analysis techniques in various industrial and commercial contexts.
The scope of this research topic is not confined to existing applications but also aims to identify and explore emerging trends and innovative AI-driven solutions that have the potential to revolutionize visual inspection processes. By fostering advancements in these areas, the research aspires to contribute significantly to the development of more intelligent, efficient, and adaptable visual inspection technologies. This, in turn, is expected to drive substantial progress across multiple sectors, ultimately leading to enhanced operational outcomes, reduced error rates, and improved overall system robustness.
Topics of interest include, but are not limited to:
1. Quality control in industrial manufacturing
2. Surface Inspection
3. Large vision models (LVMs) in visual inspection
4. Real-time visual inspection and edge computing
5. Model interpretability and trust evaluation
6. Few-shot learning and transfer learning for visual inspection
7. Medical image analysis
8. Autonomous driving and intelligent traffic monitoring
9. Crop monitoring in agriculture
10. Visual perception and human-computer interaction
11. Visual localization and object recognition
This research topic aims to rigorously investigate the future development trajectories of Artificial Intelligence (AI) in the domain of visual inspection, encompassing a comprehensive spectrum of applications. Central areas of focus include quality control and inspection, defect detection and tracking, non-destructive testing, and robotic vision for smart manufacturing, healthcare, agriculture, and autonomous vehicles. The primary objective is to enhance the operational efficiency and diagnostic accuracy of visual inspection systems through the integration of advanced AI methodologies. Furthermore, this research seeks to extend the boundaries of human capabilities through cutting-edge technologies by enabling more precise and reliable detection, classification, and analysis techniques in various industrial and commercial contexts.
The scope of this research topic is not confined to existing applications but also aims to identify and explore emerging trends and innovative AI-driven solutions that have the potential to revolutionize visual inspection processes. By fostering advancements in these areas, the research aspires to contribute significantly to the development of more intelligent, efficient, and adaptable visual inspection technologies. This, in turn, is expected to drive substantial progress across multiple sectors, ultimately leading to enhanced operational outcomes, reduced error rates, and improved overall system robustness.
Topics of interest include, but are not limited to:
1. Quality control in industrial manufacturing
2. Surface Inspection
3. Large vision models (LVMs) in visual inspection
4. Real-time visual inspection and edge computing
5. Model interpretability and trust evaluation
6. Few-shot learning and transfer learning for visual inspection
7. Medical image analysis
8. Autonomous driving and intelligent traffic monitoring
9. Crop monitoring in agriculture
10. Visual perception and human-computer interaction
11. Visual localization and object recognition
Keywords: Industrial Automation, Smart Manufacturing, Quality Control, Optical Inspection, Anomaly Detection, Machine Learning, Computer Vision, Pattern Recognition
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
