Abstract
The manufacturing industries have been searching for fresh ideas for improving product quality while cutting down on expenses and production time. Techniques for finding defects take a lot of time in production and manual quality control checks. A key element in assuring the quality of the final product is finding defects throughout the production process. It is essential to identify issues or flaws as soon as possible and take the necessary action to reduce operational and quality-related costs. Human operators are unable to accurately conduct defect detection due to their distraction and inattentiveness. The software system will cut down on human labor and the amount of time needed to find broken goods. On both a large and local scale, our system is useful. To build the application, a deep learning model along with the YOLO algorithm for better accuracy is utilized. YOLO algorithms perform really well for objects of similar color, without printing code, and visually difficult to distinguish. To evaluate the system results, Precision, Recall, and inference time metrics are utilized, the obtained value of these metrics clearly indicates the improved performance as compared to other existing methods.
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Varshney, M., Yadav, M., Bisht, M., Choudhary, K., Avasthi, S. (2023). Detecting Object Defects for Quality Assurance in Manufacturing. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_33
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DOI: https://doi.org/10.1007/978-981-99-5085-0_33
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