Abstract
In terms of efficiency, quality, and dependability, computer vision dramatically improves defect detection. In visual inspection, high-quality images necessitate excellent optical lighting systems and adequate image acquisition devices. Deep learning is having a huge impact on image analysis. Image processing and analysis are crucial technologies for gathering fault information. This study provides a systematic overview of the history of optical illumination, picture acquisition, image processing, and image analysis in the field of visual inspection. The most recent advances in computer vision-based industrial fault detection are discussed. Deep learning will become increasingly relevant as the field of visual inspection develop. As a result, a comprehensive explanation of deep learning in defect detection following the study of traditional classification, localization, and segmentation is discussed. Finally, the future of visual inspection technology is discussed. In this paper, research trends for the application of defect detection techniques in image processing are analyzed using data from Web of Science and Scopus databases. Data is analyzed globally; cluster analysis of related keywords is computed along with link strength. Various other experiments are also conducted which help analyze research trends in image processing and defect detection.
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Sachdeva, K., Aggarwal, S., Verma, A., Chawla, S. (2023). Research Trends in Image Processing and Defect Detections. In: Khanna, A., Gupta, D., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Third Doctoral Symposium on Computational Intelligence . Lecture Notes in Networks and Systems, vol 479. Springer, Singapore. https://doi.org/10.1007/978-981-19-3148-2_28
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DOI: https://doi.org/10.1007/978-981-19-3148-2_28
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