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This article presents a method for adaptive angle selection in X-ray Computed Tomography (CT) aimed at improving defect detection in industrial quality control. By utilizing Deep Reinforcement Learning (DRL), the approach incorporates prior knowledge of defect characteristics and allows for flexibility in the number of angles used during scanning, optimizing both detection accuracy and resource efficiency. The study demonstrates the feasibility of an automated process for task-specific angle selection, enha

Author
João Gabriel
Language
EN

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