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The supply of defect-free, high-quality items is a critical factor to calculate for the long-term competitiveness of industries. Quality control is important in factories to form the item defect-free as well as to meet the needs of customers. With later advancements in deep learning and computer vision advances, it has ended up identifying different flaws from the images with near-human exactness. By introducing an insightful assembling framework, defects can be limited, and human expenses can be brought down to empower feasible development. The smart production line employs production equipment, functional testing equipment, and defect detection equipment. This paper is presented with a flaw identification system for application in smart factories based on deep learning. Training with deep learning method, EfficientDet to naturally identify deformity from ultrasonic pictures. The test results show that it was able to classify flawed items rapidly with high precision in a real-world manufacturing environment. The proposed model during 5-fold cross-validation accomplished 96% of mean accuracy was a noteworthy advancement compared to a few comparative strategies that were already utilized for this task. EfficientDet detected all the flaws present in the material during assessment and performance analysis showed it was comparatively good in detecting flaws. [ABSTRACT FROM AUTHOR]